R Version Used

R.version.string
## [1] "R version 3.5.1 (2018-07-02)"

Packages Used

# Table of packages
kable(table[-1,], format = "html", align = "c") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
Package Title Maintainer Version URL
foreign Read Data Stored by ‘Minitab’, ‘S’, ‘SAS’, ‘SPSS’, ‘Stata’, ‘Systat’, ‘Weka’, ‘dBase’, … R Core Team <R-core@R-project.org>; 0.8-70 NA
outliers Tests for outliers Lukasz Komsta <lukasz.komsta@umlub.pl>; 0.14 http://www.r-project.org,
tidyverse Easily Install and Load the ‘Tidyverse’ Hadley Wickham <hadley@rstudio.com>; 1.2.1 http://tidyverse.tidyverse.org,
knitr A General-Purpose Package for Dynamic Report Generation in R Yihui Xie <xie@yihui.name>; 1.20 NA
psych Procedures for Psychological, Psychometric, and Personality Research William Revelle <revelle@northwestern.edu>; 1.8.4 NA
gvlma Global Validation of Linear Models Assumptions Elizabeth Slate <slate@stat.fsu.edu>; 1.0.0.2 NA
car Companion to Applied Regression John Fox <jfox@mcmaster.ca>; 3.0-0 https://r-forge.r-project.org/projects/car/,
ggplot2 Create Elegant Data Visualisations Using the Grammar of Graphics Hadley Wickham <hadley@rstudio.com>; 3.0.0 http://ggplot2.tidyverse.org,
GGally Extension to ‘ggplot2’ Barret Schloerke <schloerke@gmail.com>; 1.4.0 https://ggobi.github.io/ggally,
data.table Extension of data.frame Matt Dowle <mattjdowle@gmail.com>; 1.11.4 NA
kableExtra Construct Complex Table with ‘kable’ and Pipe Syntax Hao Zhu <haozhu233@gmail.com>; 0.9.0 http://haozhu233.github.io/kableExtra/,
jtools Analysis and Presentation of Social Scientific Data Jacob A. Long <long.1377@osu.edu>; 2.0.1 NA
ggstance Horizontal ‘ggplot2’ Components Lionel Henry <lionel@rstudio.com>; 0.3.1 NA
huxtable Easily Create and Style Tables for LaTeX, HTML and Other Formats David Hugh-Jones <davidhughjones@gmail.com>; 4.3.0 NA
NA NA NA NA NA
interactions Comprehensive, User-Friendly Toolkit for Probing Interactions Jacob A. Long <long.1377@osu.edu>; 1.0.0 NA

Load data.

data = read.csv('/Users/leighgayle/Box Sync/ThreatDep_Probtrack_Amygdala/Manuscript/Data&Analysis/ViolenceExposure_SocialDep_AmygdalPFCProbtrack_Data_100218_Final.csv')

In the data as it is right now, we’ve got all of the subjects with dMRI data, the violence/dep composites, and the subgroups based on the threat/dep composite scores.

Univariate (rather than multivariate) Outlier Method

Right hemisphere outliers

fit_R47 = lm(RAmy_BA47 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_R47$coefficients)-1))
plot(fit_R47, cook.levels=cutoff)

fit_R10 = lm(RAmy_BA10 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_R10$coefficients)-1))
plot(fit_R10, cook.levels=cutoff)

clean.data = data[-c(104, 108, 115, 116, 130, 151), ]

Left hemisphere outliers

fit_L11 = lm(LAmy_BA11 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_L11$coefficients)-1))
plot(fit_L11, cook.levels=cutoff)

fit_L10 = lm(LAmy_BA10 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_L10$coefficients)-1))
plot(fit_L10, cook.levels=cutoff)

clean.dataL = data[-c(22, 38, 73, 116, 131, 144), ]

Right hemisphere models

Testing amygdala-BA47 model.

# All of my covariates
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum + 
##     cm1edu + m1b2, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.12858 -0.05172 -0.01624  0.05144  0.20952 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.1116331  0.0567353   1.968 0.050779 .  
## VE              0.0083691  0.0128228   0.653 0.514870    
## Deprivation     0.0191896  0.0137379   1.397 0.164328    
## EthnoRace_C     0.0146365  0.0225407   0.649 0.517019    
## EthnoRace_AA   -0.0188106  0.0178671  -1.053 0.293959    
## Gender_0F_1M    0.0060139  0.0145778   0.413 0.680479    
## RAmy_BA10       0.5200951  0.1395541   3.727 0.000266 ***
## Internalizing   0.0135273  0.0148590   0.910 0.363944    
## pubc_mean       0.0039379  0.0120704   0.326 0.744650    
## ALES_sum       -0.0011073  0.0011051  -1.002 0.317836    
## cm1edu         -0.0004614  0.0060109  -0.077 0.938911    
## m1b2           -0.0140689  0.0149198  -0.943 0.347067    
## VE:Deprivation -0.0373562  0.0151257  -2.470 0.014534 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07242 on 166 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.1411, Adjusted R-squared:  0.07897 
## F-statistic: 2.272 on 12 and 166 DF,  p-value: 0.01078
# Preregistered covariates
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + cm1edu + m1b2, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + RAmy_BA10 + cm1edu + m1b2, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.12911 -0.05300 -0.01530  0.04955  0.21856 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.1093016  0.0350789   3.116 0.002155 ** 
## VE              0.0059499  0.0125674   0.473 0.636514    
## Deprivation     0.0220136  0.0133989   1.643 0.102255    
## EthnoRace_C     0.0170367  0.0223367   0.763 0.446691    
## EthnoRace_AA   -0.0175410  0.0177375  -0.989 0.324115    
## Gender_0F_1M    0.0028815  0.0111333   0.259 0.796093    
## RAmy_BA10       0.5301232  0.1386121   3.825 0.000184 ***
## cm1edu          0.0004848  0.0059309   0.082 0.934945    
## m1b2           -0.0130476  0.0148080  -0.881 0.379507    
## VE:Deprivation -0.0375484  0.0150525  -2.494 0.013574 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07209 on 169 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.1334, Adjusted R-squared:  0.0873 
## F-statistic: 2.892 on 9 and 169 DF,  p-value: 0.003323
# Not adjusting for RAmy_BA10
# All of my covariates
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + 
##     m1b2, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.11937 -0.05567 -0.02096  0.05154  0.22100 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     0.143878   0.058195   2.472  0.01443 * 
## VE              0.009247   0.013306   0.695  0.48805   
## Deprivation     0.019851   0.014257   1.392  0.16567   
## EthnoRace_C     0.014463   0.023394   0.618  0.53726   
## EthnoRace_AA   -0.008562   0.018323  -0.467  0.64091   
## Gender_0F_1M    0.005922   0.015130   0.391  0.69597   
## Internalizing   0.016199   0.015404   1.052  0.29448   
## pubc_mean       0.003325   0.012526   0.265  0.79102   
## ALES_sum       -0.001339   0.001145  -1.169  0.24402   
## cm1edu         -0.003299   0.006188  -0.533  0.59466   
## m1b2           -0.016231   0.015473  -1.049  0.29570   
## VE:Deprivation -0.044704   0.015565  -2.872  0.00461 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07516 on 167 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.0692, Adjusted R-squared:  0.007885 
## F-statistic: 1.129 on 11 and 167 DF,  p-value: 0.342
# No covariates included
summary(lm(RAmy_BA47 ~ VE * Deprivation, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10242 -0.05713 -0.01939  0.04961  0.22930 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.103768   0.005771  17.980  < 2e-16 ***
## VE              0.001060   0.012183   0.087  0.93076    
## Deprivation     0.020694   0.013462   1.537  0.12600    
## VE:Deprivation -0.038615   0.014649  -2.636  0.00913 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07431 on 179 degrees of freedom
## Multiple R-squared:  0.03983,    Adjusted R-squared:  0.02374 
## F-statistic: 2.475 on 3 and 179 DF,  p-value: 0.06306
# Correlations between amygdala-BA47 probtrack and environmental composites.
cor.test(clean.data$RAmy_BA47, clean.data$VE)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA47 and clean.data$VE
## t = -0.19033, df = 181, p-value = 0.8493
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1588767  0.1311798
## sample estimates:
##         cor 
## -0.01414606
cor.test(clean.data$RAmy_BA47, clean.data$Deprivation)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA47 and clean.data$Deprivation
## t = 0.47278, df = 181, p-value = 0.6369
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.110500  0.179263
## sample estimates:
##        cor 
## 0.03511956
# Correlation between amyygdala-BA47 probtrack and amygdala activation to threat faces.
cor.test(clean.data$Ramy_0035, clean.data$RAmy_BA47)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_0035 and clean.data$RAmy_BA47
## t = -3.712, df = 150, p-value = 0.0002892
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4294252 -0.1371882
## sample estimates:
##       cor 
## -0.290054
# Including covariates. 
summary(lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, 
##     data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.57781 -0.39097  0.03322  0.34232  1.93497 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.20961    0.30398   3.979 0.000108 ***
## RAmy_BA47     -2.64476    0.69467  -3.807 0.000205 ***
## Internalizing  0.18762    0.12282   1.528 0.128743    
## pubc_mean     -0.03291    0.09005  -0.365 0.715299    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6428 on 148 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.09877,    Adjusted R-squared:  0.0805 
## F-statistic: 5.407 on 3 and 148 DF,  p-value: 0.001475
actmod1 = lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data = clean.data)
lm.beta(actmod1)
##     RAmy_BA47 Internalizing     pubc_mean 
##   -0.29776239    0.11973725   -0.02858212
summary(lm(Ramy_0035 ~ RAmy_BA47 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + 
##     m1b2, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.53444 -0.38330  0.04329  0.35252  2.03119 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.61727    0.54665   1.129 0.260761    
## RAmy_BA47     -2.59122    0.70519  -3.674 0.000339 ***
## EthnoRace_C    0.02034    0.20578   0.099 0.921391    
## EthnoRace_AA   0.04680    0.16286   0.287 0.774288    
## Gender_0F_1M   0.18210    0.15019   1.212 0.227388    
## Internalizing  0.18179    0.14546   1.250 0.213471    
## pubc_mean      0.06690    0.11977   0.559 0.577370    
## ALES_sum       0.01712    0.01060   1.616 0.108392    
## cm1edu        -0.08734    0.05658  -1.544 0.124921    
## m1b2           0.08718    0.14604   0.597 0.551521    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6427 on 139 degrees of freedom
##   (34 observations deleted due to missingness)
## Multiple R-squared:  0.1514, Adjusted R-squared:  0.09649 
## F-statistic: 2.756 on 9 and 139 DF,  p-value: 0.005382
RBA47_act_mod = lm(Ramy_0035 ~ RAmy_BA47 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data)
lm.beta(RBA47_act_mod)
##     RAmy_BA47   EthnoRace_C  EthnoRace_AA  Gender_0F_1M Internalizing 
##   -0.29113279    0.01071021    0.03124486    0.13460849    0.11346563 
##     pubc_mean      ALES_sum        cm1edu          m1b2 
##    0.05786410    0.13877999   -0.13017061    0.05054025
# Plot association between amygdala-BA47 connectivity and amygdala activation to threat faces.
plot(clean.data$RAmy_BA47, clean.data$Ramy_0035, xlab = 'Amygdala-OFC White Matter Connectivity', ylab = 'Amygdala Activation', frame.plot = FALSE)
abline(lm(clean.data$Ramy_0035 ~ clean.data$RAmy_BA47))

# Check the correlation between amygdala-BA47 probtrack and amygdala activation to angry faces and to fearful faces.
cor.test(clean.data$RAmy_0011, clean.data$RAmy_BA47)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_0011 and clean.data$RAmy_BA47
## t = -2.6634, df = 150, p-value = 0.00858
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.35953783 -0.05516595
## sample estimates:
##        cor 
## -0.2125004
summary(lm(RAmy_0011 ~ RAmy_BA47, data = clean.data))
## 
## Call:
## lm(formula = RAmy_0011 ~ RAmy_BA47, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.18775 -0.23439 -0.01549  0.25184  0.92374 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.54245    0.05447   9.959  < 2e-16 ***
## RAmy_BA47   -1.14056    0.42823  -2.663  0.00858 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3971 on 150 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.04516,    Adjusted R-squared:  0.03879 
## F-statistic: 7.094 on 1 and 150 DF,  p-value: 0.00858
cor.test(clean.data$Ramy_0003, clean.data$RAmy_BA47)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_0003 and clean.data$RAmy_BA47
## t = -1.9561, df = 150, p-value = 0.05231
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.309155503  0.001521612
## sample estimates:
##        cor 
## -0.1577173
summary(lm(Ramy_0003 ~ RAmy_BA47, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0003 ~ RAmy_BA47, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.32673 -0.22264 -0.00822  0.19260  1.33566 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.45027    0.05546   8.119 1.59e-13 ***
## RAmy_BA47   -0.85291    0.43602  -1.956   0.0523 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4044 on 150 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.02487,    Adjusted R-squared:  0.01837 
## F-statistic: 3.826 on 1 and 150 DF,  p-value: 0.05231
summary(lm(Ramy_0003 ~ RAmy_BA47 + Internalizing + pubc_mean, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0003 ~ RAmy_BA47 + Internalizing + pubc_mean, 
##     data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.35948 -0.23180 -0.02618  0.20078  1.27111 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.72460    0.19077   3.798 0.000212 ***
## RAmy_BA47     -0.86961    0.43596  -1.995 0.047913 *  
## Internalizing  0.06074    0.07708   0.788 0.431928    
## pubc_mean     -0.08454    0.05652  -1.496 0.136831    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4034 on 148 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.04243,    Adjusted R-squared:  0.02302 
## F-statistic: 2.186 on 3 and 148 DF,  p-value: 0.09211

Show that the residuals of this model look random for Amygdala-BA47 model.

mod = summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum, data=clean.data))
fitBA47 = lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data)
fit_int = lm(RAmy_BA47 ~ VE * Deprivation, data = clean.data)
fit_prereg = lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + cm1edu + m1b2, data=clean.data)
# To get the beta weights
lm.beta(fitBA47)
## Warning in b * sx: longer object length is not a multiple of shorter object
## length
##             VE    Deprivation    EthnoRace_C   EthnoRace_AA   Gender_0F_1M 
##    0.059253261    0.128960058    0.067425997   -0.110001820    0.039820543 
##      RAmy_BA10  Internalizing      pubc_mean       ALES_sum         cm1edu 
##    0.281265469    0.074013282    0.030468099   -0.078266731   -0.006296673 
##           m1b2 VE:Deprivation 
##   -0.076457958   -0.264482541
lm.beta(fit_int)
## Warning in b * sx: longer object length is not a multiple of shorter object
## length
##             VE    Deprivation VE:Deprivation 
##    0.007518361    0.138282871   -0.273872284
lm.beta(fit_prereg)
## Warning in b * sx: longer object length is not a multiple of shorter object
## length
##             VE    Deprivation    EthnoRace_C   EthnoRace_AA   Gender_0F_1M 
##     0.04212513     0.14793806     0.07848330    -0.10257709     0.01907941 
##      RAmy_BA10         cm1edu           m1b2 VE:Deprivation 
##     0.28668864     0.00661693    -0.07090731    -0.26584321
plot(mod$residuals)

mean(mod$residuals) # the mean of the residuals are zero
## [1] -3.277231e-18
t.test(mod$residuals)
## 
##  One Sample t-test
## 
## data:  mod$residuals
## t = -6.3873e-16, df = 182, p-value = 1
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.0101236  0.0101236
## sample estimates:
##     mean of x 
## -3.277231e-18
dwtest(fitBA47)
## 
##  Durbin-Watson test
## 
## data:  fitBA47
## DW = 1.918, p-value = 0.2667
## alternative hypothesis: true autocorrelation is greater than 0
cor.test(mod$residuals, clean.data$Deprivation*clean.data$VE)
## 
##  Pearson's product-moment correlation
## 
## data:  mod$residuals and clean.data$Deprivation * clean.data$VE
## t = 2.1481e-16, df = 181, p-value = 1
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1450566  0.1450566
## sample estimates:
##          cor 
## 1.596658e-17
par(family = 'serif')
qqnorm(mod$residuals, frame.plot = FALSE, main=NULL, cex.lab = 1.5, font.lab=2)

Testing amygdala-BA10 model.

# All of my covariates
summary(lm(RAmy_BA10 ~ VE * Deprivation + RAmy_BA47 + EthnoRace_AA + EthnoRace_C + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + RAmy_BA47 + EthnoRace_AA + 
##     EthnoRace_C + Gender_0F_1M + Internalizing + pubc_mean + 
##     ALES_sum + cm1edu + m1b2, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.053192 -0.029054 -0.005352  0.016847  0.124212 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.0406384  0.0305004   1.332 0.184558    
## VE              0.0003154  0.0068595   0.046 0.963379    
## Deprivation    -0.0016757  0.0073815  -0.227 0.820698    
## RAmy_BA47       0.1484542  0.0398338   3.727 0.000266 ***
## EthnoRace_AA    0.0209766  0.0094382   2.223 0.027598 *  
## EthnoRace_C    -0.0024801  0.0120564  -0.206 0.837274    
## Gender_0F_1M   -0.0010549  0.0077919  -0.135 0.892470    
## Internalizing   0.0027326  0.0079556   0.343 0.731668    
## pubc_mean      -0.0016726  0.0064495  -0.259 0.795694    
## ALES_sum       -0.0002465  0.0005919  -0.417 0.677570    
## cm1edu         -0.0049664  0.0031882  -1.558 0.121203    
## m1b2           -0.0017475  0.0079913  -0.219 0.827169    
## VE:Deprivation -0.0074915  0.0082077  -0.913 0.362698    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03869 on 166 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.1617, Adjusted R-squared:  0.1011 
## F-statistic: 2.668 on 12 and 166 DF,  p-value: 0.00266
# Not adjusting for RAmy_BA47
# All of my covariates
summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + 
##     Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + 
##     m1b2, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.058827 -0.031378 -0.009747  0.016993  0.123887 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.0619976  0.0310916   1.994   0.0478 *
## VE              0.0016882  0.0071090   0.237   0.8126  
## Deprivation     0.0012713  0.0076170   0.167   0.8677  
## EthnoRace_AA    0.0197056  0.0097892   2.013   0.0457 *
## EthnoRace_C    -0.0003329  0.0124987  -0.027   0.9788  
## Gender_0F_1M   -0.0001757  0.0080833  -0.022   0.9827  
## Internalizing   0.0051375  0.0082297   0.624   0.5333  
## pubc_mean      -0.0011791  0.0066924  -0.176   0.8604  
## ALES_sum       -0.0004453  0.0006118  -0.728   0.4677  
## cm1edu         -0.0054562  0.0033062  -1.650   0.1008  
## m1b2           -0.0041571  0.0082667  -0.503   0.6157  
## VE:Deprivation -0.0141280  0.0083156  -1.699   0.0912 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04015 on 167 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.09155,    Adjusted R-squared:  0.03171 
## F-statistic:  1.53 on 11 and 167 DF,  p-value: 0.1249
# No covariates
summary(lm(RAmy_BA10 ~ VE * Deprivation, data = clean.data))
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.052020 -0.033268 -0.009264  0.023304  0.124582 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.050212   0.003184  15.772   <2e-16 ***
## VE              0.005649   0.006721   0.841    0.402    
## Deprivation     0.006325   0.007426   0.852    0.396    
## VE:Deprivation -0.020776   0.008081  -2.571    0.011 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.041 on 179 degrees of freedom
## Multiple R-squared:  0.03612,    Adjusted R-squared:  0.01996 
## F-statistic: 2.236 on 3 and 179 DF,  p-value: 0.0857
# Correlation between amyygdala-BA10 probtrack and amygdala activation to threat faces.
cor.test(clean.data$Ramy_0035, clean.data$RAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_0035 and clean.data$RAmy_BA10
## t = -2.5913, df = 150, p-value = 0.01051
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.35451431 -0.04942427
## sample estimates:
##        cor 
## -0.2069962
cor.test(clean.data$Ramy_0003, clean.data$RAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_0003 and clean.data$RAmy_BA10
## t = -2.0829, df = 150, p-value = 0.03896
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.318363596 -0.008691851
## sample estimates:
##        cor 
## -0.1676606
cor.test(clean.data$RAmy_0011, clean.data$RAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_0011 and clean.data$RAmy_BA10
## t = -2.3473, df = 150, p-value = 0.02022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.33732032 -0.02992375
## sample estimates:
##        cor 
## -0.1882278
summary(lm(RAmy_0011 ~ RAmy_BA10, data = clean.data))
## 
## Call:
## lm(formula = RAmy_0011 ~ RAmy_BA10, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.10669 -0.22345 -0.01279  0.23598  0.94944 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.51278    0.04931  10.398   <2e-16 ***
## RAmy_BA10   -1.86684    0.79532  -2.347   0.0202 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3991 on 150 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.03543,    Adjusted R-squared:  0.029 
## F-statistic:  5.51 on 1 and 150 DF,  p-value: 0.02022
summary(lm(Ramy_0003 ~ RAmy_BA10, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0003 ~ RAmy_BA10, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.29170 -0.22899 -0.02281  0.22370  1.34640 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.44114    0.04987   8.845 2.33e-15 ***
## RAmy_BA10   -1.67540    0.80436  -2.083    0.039 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4037 on 150 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.02811,    Adjusted R-squared:  0.02163 
## F-statistic: 4.338 on 1 and 150 DF,  p-value: 0.03896
# Including covariates. 
summary(lm(Ramy_0035 ~ RAmy_BA10 +  Internalizing + pubc_mean, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, 
##     data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8839 -0.3438 -0.0134  0.3413  1.9457 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.12478    0.31059   3.621 0.000402 ***
## RAmy_BA10     -3.46554    1.31250  -2.640 0.009169 ** 
## Internalizing  0.16775    0.12556   1.336 0.183597    
## pubc_mean     -0.04034    0.09224  -0.437 0.662457    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6582 on 148 degrees of freedom
##   (31 observations deleted due to missingness)
## Multiple R-squared:  0.05502,    Adjusted R-squared:  0.03586 
## F-statistic: 2.872 on 3 and 148 DF,  p-value: 0.03838
actmod2 = lm(Ramy_0035 ~ RAmy_BA10 +  Internalizing + pubc_mean, data = clean.data)
lm.beta(actmod2)
##     RAmy_BA10 Internalizing     pubc_mean 
##   -0.21115053    0.10705551   -0.03503934
summary(lm(Ramy_0035 ~ RAmy_BA10 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + 
##     m1b2, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.80557 -0.36918  0.01096  0.33275  2.08432 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.465308   0.549225   0.847  0.39834   
## RAmy_BA10     -4.214961   1.364128  -3.090  0.00242 **
## EthnoRace_C   -0.002073   0.208587  -0.010  0.99209   
## EthnoRace_AA   0.103482   0.167135   0.619  0.53683   
## Gender_0F_1M   0.161582   0.151980   1.063  0.28954   
## Internalizing  0.138989   0.146566   0.948  0.34462   
## pubc_mean      0.046589   0.121293   0.384  0.70149   
## ALES_sum       0.020123   0.010681   1.884  0.06165 . 
## cm1edu        -0.094702   0.057615  -1.644  0.10250   
## m1b2           0.145896   0.147381   0.990  0.32393   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6512 on 139 degrees of freedom
##   (34 observations deleted due to missingness)
## Multiple R-squared:  0.1288, Adjusted R-squared:  0.07243 
## F-statistic: 2.284 on 9 and 139 DF,  p-value: 0.02022
RBA10_act_mod = lm(Ramy_0035 ~ RAmy_BA10 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data)
lm.beta(RBA10_act_mod)
##     RAmy_BA10   EthnoRace_C  EthnoRace_AA  Gender_0F_1M Internalizing 
##  -0.255566513  -0.001091307   0.069093890   0.119438883   0.086750104 
##     pubc_mean      ALES_sum        cm1edu          m1b2 
##   0.040297079   0.163073476  -0.141144444   0.084580538

Looking at the residuals of the amygdala-BA10 model.

mod1 = summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA+ EthnoRace_C + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))
mod10 = lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA+ EthnoRace_C + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data)

mod_int = lm(RAmy_BA10 ~ VE * Deprivation, data = clean.data)
summary(mod_int)
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.052020 -0.033268 -0.009264  0.023304  0.124582 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.050212   0.003184  15.772   <2e-16 ***
## VE              0.005649   0.006721   0.841    0.402    
## Deprivation     0.006325   0.007426   0.852    0.396    
## VE:Deprivation -0.020776   0.008081  -2.571    0.011 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.041 on 179 degrees of freedom
## Multiple R-squared:  0.03612,    Adjusted R-squared:  0.01996 
## F-statistic: 2.236 on 3 and 179 DF,  p-value: 0.0857
plot(mod1$residuals)

Just a fun plot of what the RAmy_BA10 interaction looks like before covariates – it seems to look really similar to the RAmy-BA47 interaction. That’s a fun sanity check.

interact_plot(mod_int, pred = VE, modx = Deprivation, data = clean.data, modx.values = 'plus-minus')

Left hemisphere models

Amygdala-BA10 model

# Including all covariates
summary(lm(LAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + Gender_0F_1M + LAmy_BA11 + Internalizing + pubc_mean + CTQ_Abuse + CTQ.Neglect, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + 
##     Gender_0F_1M + LAmy_BA11 + Internalizing + pubc_mean + CTQ_Abuse + 
##     CTQ.Neglect, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.026873 -0.005284 -0.002192  0.000822  0.057775 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     8.384e-03  8.976e-03   0.934    0.352    
## VE              1.127e-03  2.266e-03   0.497    0.620    
## Deprivation    -5.298e-04  2.575e-03  -0.206    0.837    
## EthnoRace_AA    3.081e-03  3.083e-03   0.999    0.319    
## EthnoRace_C     3.145e-03  3.870e-03   0.813    0.418    
## Gender_0F_1M   -3.440e-03  2.513e-03  -1.369    0.173    
## LAmy_BA11       3.840e-01  3.975e-02   9.661   <2e-16 ***
## Internalizing   2.726e-03  2.596e-03   1.050    0.295    
## pubc_mean      -2.198e-03  2.136e-03  -1.029    0.305    
## CTQ_Abuse      -5.525e-05  1.901e-04  -0.291    0.772    
## CTQ.Neglect     1.129e-04  1.701e-04   0.664    0.508    
## VE:Deprivation -2.561e-03  2.649e-03  -0.967    0.335    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01261 on 165 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3975, Adjusted R-squared:  0.3573 
## F-statistic: 9.896 on 11 and 165 DF,  p-value: 1.121e-13
# No covariates
summary(lm(LAmy_BA10 ~ VE * Deprivation, data = clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA10 ~ VE * Deprivation, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.011388 -0.008976 -0.006239  0.002917  0.069315 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.085e-02  1.212e-03   8.958 4.24e-16 ***
## VE              3.945e-05  2.575e-03   0.015    0.988    
## Deprivation     1.042e-03  3.036e-03   0.343    0.732    
## VE:Deprivation -4.285e-03  3.031e-03  -1.414    0.159    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01557 on 179 degrees of freedom
## Multiple R-squared:  0.01196,    Adjusted R-squared:  -0.004596 
## F-statistic: 0.7225 on 3 and 179 DF,  p-value: 0.5398
# Association between amygdala-BA10 and amygdala activation to threat faces.
cor.test(clean.dataL$Lamy_0035, clean.dataL$LAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_0035 and clean.dataL$LAmy_BA10
## t = -3.9734, df = 151, p-value = 0.0001094
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4446393 -0.1566375
## sample estimates:
##        cor 
## -0.3076685
# Including covariates.
summary(lm(Lamy_0035 ~ LAmy_BA10 + Internalizing + pubc_mean, data = clean.dataL))
## 
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA10 + Internalizing + pubc_mean, 
##     data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.54665 -0.37617 -0.06264  0.31400  1.37916 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.03211    0.25861   3.991 0.000103 ***
## LAmy_BA10     -12.65472    3.03595  -4.168 5.18e-05 ***
## Internalizing   0.19112    0.10908   1.752 0.081811 .  
## pubc_mean      -0.03754    0.07794  -0.482 0.630746    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5634 on 149 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.1138, Adjusted R-squared:  0.09597 
## F-statistic: 6.379 on 3 and 149 DF,  p-value: 0.0004269
actmod3 = lm(Lamy_0035 ~ LAmy_BA10 + Internalizing + pubc_mean, data = clean.dataL)
lm.beta(actmod3)
##     LAmy_BA10 Internalizing     pubc_mean 
##    -0.3243390     0.1364928    -0.0372194
summary(lm(Lamy_0035 ~ LAmy_BA10 + EthnoRace_AA + EthnoRace_C + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL))
## 
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA10 + EthnoRace_AA + EthnoRace_C + 
##     Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + 
##     m1b2, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42218 -0.38257 -0.01476  0.28558  1.43171 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.704643   0.475121   1.483    0.140    
## LAmy_BA10     -12.748139   3.160210  -4.034 8.98e-05 ***
## EthnoRace_AA    0.174127   0.140207   1.242    0.216    
## EthnoRace_C     0.117996   0.178041   0.663    0.509    
## Gender_0F_1M    0.083592   0.130791   0.639    0.524    
## Internalizing   0.176301   0.129938   1.357    0.177    
## pubc_mean       0.005813   0.104104   0.056    0.956    
## ALES_sum        0.012061   0.009424   1.280    0.203    
## cm1edu         -0.073627   0.049221  -1.496    0.137    
## m1b2            0.024669   0.127816   0.193    0.847    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.566 on 140 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.1568, Adjusted R-squared:  0.1026 
## F-statistic: 2.893 on 9 and 140 DF,  p-value: 0.003622
LBA10_act_mod = lm(Lamy_0035 ~ LAmy_BA10 + EthnoRace_AA + EthnoRace_C + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL)
lm.beta(LBA10_act_mod)
##     LAmy_BA10  EthnoRace_AA   EthnoRace_C  Gender_0F_1M Internalizing 
##  -0.326768500   0.132235389   0.070103193   0.069966237   0.123104466 
##     pubc_mean      ALES_sum        cm1edu          m1b2 
##   0.005741941   0.110948372  -0.124587967   0.016142258
summary(lm(LAmy_0011 ~ LAmy_BA10, data = clean.dataL))
## 
## Call:
## lm(formula = LAmy_0011 ~ LAmy_BA10, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.05174 -0.18910 -0.04617  0.22286  1.09043 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.45078    0.03549  12.702   <2e-16 ***
## LAmy_BA10   -5.53465    1.93360  -2.862   0.0048 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3621 on 151 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.05147,    Adjusted R-squared:  0.04518 
## F-statistic: 8.193 on 1 and 151 DF,  p-value: 0.004803
summary(lm(Lamy_0003 ~ LAmy_BA10, data = clean.dataL))
## 
## Call:
## lm(formula = Lamy_0003 ~ LAmy_BA10, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43252 -0.23424 -0.03496  0.24835  1.22662 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.47800    0.03974   12.03  < 2e-16 ***
## LAmy_BA10   -6.47473    2.16536   -2.99  0.00326 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4055 on 151 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.0559, Adjusted R-squared:  0.04965 
## F-statistic: 8.941 on 1 and 151 DF,  p-value: 0.003257

Checking the residuals of the amygdala-BA10 model

mod2 = summary(lm(LAmy_BA10 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + LAmy_BA11 + Internalizing + pubc_mean, data=clean.dataL))

plot(mod2$residuals)

Amygdala-BA11 Model

# Including all covariates.
summary(lm(LAmy_BA11 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + Gender_1F_2M + LAmy_BA10 + Internalizing + pubc_mean + CTQ_Abuse + CTQ.Neglect, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA11 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + 
##     Gender_1F_2M + LAmy_BA10 + Internalizing + pubc_mean + CTQ_Abuse + 
##     CTQ.Neglect, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.048043 -0.009621 -0.004978  0.003858  0.082360 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.0010769  0.0167095  -0.064    0.949    
## VE             -0.0019776  0.0035458  -0.558    0.578    
## Deprivation     0.0006865  0.0040309   0.170    0.865    
## EthnoRace_AA    0.0019664  0.0048372   0.407    0.685    
## EthnoRace_C     0.0002974  0.0060692   0.049    0.961    
## Gender_1F_2M    0.0052831  0.0039343   1.343    0.181    
## LAmy_BA10       0.9408491  0.0973833   9.661   <2e-16 ***
## Internalizing   0.0025730  0.0040721   0.632    0.528    
## pubc_mean       0.0033019  0.0033442   0.987    0.325    
## CTQ_Abuse      -0.0003311  0.0002964  -1.117    0.266    
## CTQ.Neglect    -0.0001699  0.0002663  -0.638    0.524    
## VE:Deprivation -0.0008885  0.0041569  -0.214    0.831    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01973 on 165 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3916, Adjusted R-squared:  0.351 
## F-statistic: 9.653 on 11 and 165 DF,  p-value: 2.353e-13
# No covariates.
summary(lm(LAmy_BA11 ~ VE * Deprivation, data = clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA11 ~ VE * Deprivation, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.020188 -0.015841 -0.009986  0.006083  0.092431 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.019927   0.001898  10.502   <2e-16 ***
## VE             -0.001851   0.004032  -0.459    0.647    
## Deprivation     0.001581   0.004754   0.333    0.740    
## VE:Deprivation -0.005120   0.004746  -1.079    0.282    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02438 on 179 degrees of freedom
## Multiple R-squared:  0.01017,    Adjusted R-squared:  -0.006421 
## F-statistic: 0.6129 on 3 and 179 DF,  p-value: 0.6075
# Association between amygdala-BA11 connectivity and amygdala activation to threat faces.
cor.test(clean.dataL$Lamy_0035, clean.dataL$LAmy_BA11)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_0035 and clean.dataL$LAmy_BA11
## t = -3.4424, df = 151, p-value = 0.0007461
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4108459 -0.1160426
## sample estimates:
##        cor 
## -0.2697536
# Including covariates.
summary(lm(Lamy_0035 ~ LAmy_BA11 + Internalizing + pubc_mean, data = clean.dataL))
## 
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA11 + Internalizing + pubc_mean, 
##     data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.61737 -0.38847 -0.04459  0.30089  1.51358 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.99552    0.26242   3.794 0.000215 ***
## LAmy_BA11     -6.31347    1.82545  -3.459 0.000708 ***
## Internalizing  0.14580    0.11004   1.325 0.187215    
## pubc_mean     -0.02731    0.07939  -0.344 0.731349    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5728 on 149 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.08401,    Adjusted R-squared:  0.06557 
## F-statistic: 4.555 on 3 and 149 DF,  p-value: 0.004387
actmod4 = lm(Lamy_0035 ~ LAmy_BA11 + Internalizing + pubc_mean, data = clean.dataL)
lm.beta(actmod4)
##     LAmy_BA11 Internalizing     pubc_mean 
##   -0.27197503    0.10412214   -0.02707385
summary(lm(Lamy_0035 ~ LAmy_BA11 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL))
## 
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA11 + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + 
##     m1b2, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56844 -0.35610 -0.02711  0.27057  1.44881 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.542496   0.475775   1.140 0.256135    
## LAmy_BA11     -6.823881   1.867726  -3.654 0.000365 ***
## EthnoRace_C    0.109707   0.179681   0.611 0.542478    
## EthnoRace_AA   0.155514   0.141137   1.102 0.272410    
## Gender_0F_1M   0.164690   0.132415   1.244 0.215672    
## Internalizing  0.129310   0.129718   0.997 0.320555    
## pubc_mean      0.062969   0.105764   0.595 0.552559    
## ALES_sum       0.016969   0.009355   1.814 0.071815 .  
## cm1edu        -0.080555   0.049883  -1.615 0.108591    
## m1b2          -0.018202   0.130031  -0.140 0.888878    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5713 on 140 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.1407, Adjusted R-squared:  0.08551 
## F-statistic: 2.548 on 9 and 140 DF,  p-value: 0.009679
LBA11_act_mod = lm(Lamy_0035 ~ LAmy_BA11 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M  + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL)
lm.beta(LBA11_act_mod)
##     LAmy_BA11   EthnoRace_C  EthnoRace_AA  Gender_0F_1M Internalizing 
##   -0.29333960    0.06517835    0.11810029    0.13784408    0.09029230 
##     pubc_mean      ALES_sum        cm1edu          m1b2 
##    0.06219860    0.15609964   -0.13631212   -0.01191030
summary(lm(LAmy_0011 ~ LAmy_BA11, data = clean.dataL))
## 
## Call:
## lm(formula = LAmy_0011 ~ LAmy_BA11, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.00472 -0.20670 -0.04196  0.23181  1.10276 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.43874    0.03791  11.574   <2e-16 ***
## LAmy_BA11   -2.24835    1.16695  -1.927   0.0559 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3673 on 151 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.02399,    Adjusted R-squared:  0.01753 
## F-statistic: 3.712 on 1 and 151 DF,  p-value: 0.0559
summary(lm(Lamy_0003 ~ LAmy_BA11, data = clean.dataL))
## 
## Call:
## lm(formula = Lamy_0003 ~ LAmy_BA11, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.33227 -0.26554 -0.04716  0.27339  1.21842 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.49688    0.04157   11.95  < 2e-16 ***
## LAmy_BA11   -4.26201    1.27972   -3.33  0.00109 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4028 on 151 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.06843,    Adjusted R-squared:  0.06226 
## F-statistic: 11.09 on 1 and 151 DF,  p-value: 0.00109

Checking the residuals for the amygdala-BA11 model.

mod3 = summary(lm(LAmy_BA11 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + LAmy_BA10 + Internalizing, data=clean.dataL))

plot(mod3$residuals)

Parsing the interaction for RAmy_BA47.

Calculating basic statistics to parse the interaction.

model = lm(RAmy_BA47 ~ VE * Deprivation, data = clean.data)
model2 = lm(clean.data$RAmy_BA47  ~ clean.data$Deprivation * clean.data$VE)

vcov(model)
##                  (Intercept)            VE   Deprivation VE:Deprivation
## (Intercept)     3.330648e-05  1.236356e-06  6.341440e-06  -2.517358e-05
## VE              1.236356e-06  1.484268e-04 -6.211801e-05  -3.870033e-05
## Deprivation     6.341440e-06 -6.211801e-05  1.812183e-04  -6.851916e-05
## VE:Deprivation -2.517358e-05 -3.870033e-05 -6.851916e-05   2.145924e-04
summary(model)
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10242 -0.05713 -0.01939  0.04961  0.22930 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.103768   0.005771  17.980  < 2e-16 ***
## VE              0.001060   0.012183   0.087  0.93076    
## Deprivation     0.020694   0.013462   1.537  0.12600    
## VE:Deprivation -0.038615   0.014649  -2.636  0.00913 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07431 on 179 degrees of freedom
## Multiple R-squared:  0.03983,    Adjusted R-squared:  0.02374 
## F-statistic: 2.475 on 3 and 179 DF,  p-value: 0.06306
vcov(model2)
##                                        (Intercept) clean.data$Deprivation
## (Intercept)                           3.330648e-05           6.341440e-06
## clean.data$Deprivation                6.341440e-06           1.812183e-04
## clean.data$VE                         1.236356e-06          -6.211801e-05
## clean.data$Deprivation:clean.data$VE -2.517358e-05          -6.851916e-05
##                                      clean.data$VE
## (Intercept)                           1.236356e-06
## clean.data$Deprivation               -6.211801e-05
## clean.data$VE                         1.484268e-04
## clean.data$Deprivation:clean.data$VE -3.870033e-05
##                                      clean.data$Deprivation:clean.data$VE
## (Intercept)                                                 -2.517358e-05
## clean.data$Deprivation                                      -6.851916e-05
## clean.data$VE                                               -3.870033e-05
## clean.data$Deprivation:clean.data$VE                         2.145924e-04
summary(model2)
## 
## Call:
## lm(formula = clean.data$RAmy_BA47 ~ clean.data$Deprivation * 
##     clean.data$VE)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10242 -0.05713 -0.01939  0.04961  0.22930 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)                           0.103768   0.005771  17.980  < 2e-16
## clean.data$Deprivation                0.020694   0.013462   1.537  0.12600
## clean.data$VE                         0.001060   0.012183   0.087  0.93076
## clean.data$Deprivation:clean.data$VE -0.038615   0.014649  -2.636  0.00913
##                                         
## (Intercept)                          ***
## clean.data$Deprivation                  
## clean.data$VE                           
## clean.data$Deprivation:clean.data$VE ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07431 on 179 degrees of freedom
## Multiple R-squared:  0.03983,    Adjusted R-squared:  0.02374 
## F-statistic: 2.475 on 3 and 179 DF,  p-value: 0.06306
min(clean.data$VE)
## [1] -0.8200771
max(clean.data$VE)
## [1] 2.169539
mean(clean.data$VE)
## [1] 0.0384145
describe(clean.data$VE)
vars n mean sd median trimmed mad min max range skew kurtosis se
1 183 0.0384 0.533 -0.0283 -0.0166 0.509 -0.82 2.17 2.99 1.08 1.61 0.0394
max(clean.data$Deprivation)
## [1] 2.671064
min(clean.data$Deprivation)
## [1] -0.7646478
mean(clean.data$Deprivation)
## [1] 0.02860156
describe(clean.data$Deprivation)
vars n mean sd median trimmed mad min max range skew kurtosis se
1 183 0.0286 0.503 -0.0469 -0.0253 0.405 -0.765 2.67 3.44 1.61 5.04 0.0372

Interaction plots/statistics from Preacher Website - http://www.quantpsy.org/interact/mlr2.htm

Social Deprivation as Moderator

     TWO-WAY INTERACTION SIMPLE SLOPES OUTPUT

Your Input

X1 = -0.8200771 X2 = 2.169539 cv1 = -0.47 cv2 = 0.03 cv3 = 0.8 Intercept = 0.103768 X Slope = 0.00106 Z Slope = 0.020694 XZ Slope = -0.038615 df = 166 alpha = 0.05

Asymptotic (Co)variances

var(b0) 0.00003331 var(b1) 0.00014843 var(b2) 0.00018122 var(b3) 0.00021459 cov(b2,b0) 0.00000634 cov(b3,b1) -0.0000387

Region of Significance

Z at lower bound of region = -1.122 Z at upper bound of region = 0.7862 (simple slopes are significant outside this region.)

Simple Intercepts and Slopes at Conditional Values of Z

At Z = cv1… simple intercept = 0.094(0.0082), t=11.4569, p=0 simple slope = 0.0192(0.0152), t=1.2606, p=0.2092 At Z = cv2… simple intercept = 0.1044(0.0058), t=17.9421, p=0 simple slope = -0.0001(0.0121), t=-0.0081, p=0.9935 At Z = cv3… simple intercept = 0.1203(0.0126), t=9.5293, p=0 simple slope = -0.0298(0.015), t=-1.9939, p=0.0478

Simple Intercepts and Slopes at Region Boundaries

Lower Bound…
simple intercept = 0.0805(0.0157), t=5.1231, p=0 simple slope = 0.0444(0.0225), t=1.9743, p=0.05 Upper Bound…
simple intercept = 0.12(0.0125), t=9.6326, p=0 simple slope = -0.0293(0.0148), t=-1.9744, p=0.05

Points to Plot

Line for cv1: From {X=-0.8201, Y=0.0783} to {X=2.1695, Y=0.1357} Line for cv2: From {X=-0.8201, Y=0.1045} to {X=2.1695, Y=0.1042} Line for cv3: From {X=-0.8201, Y=0.1448} to {X=2.1695, Y=0.0556}

xx <- c(-0.8201,2.1695)   #  <-- change to alter plot dims
yy <- c(0.0378,0.1448)   #  <-- change to alter plot dims
leg <- c(-0.8201,0.0511)   #  <-- change to alter legend location
x <- c(-0.8201,2.1695)   #  <-- x-coords for lines
y1 <- c(0.0783,0.1357)
y2 <- c(0.1045,0.1042)
y3 <- c(0.1448,0.0556)
plot(xx,yy,type='n',font=2,font.lab=2,xlab='X',ylab='Y',main='MLR 2-Way Interaction Plot')
lines(x,y1,lwd=3,lty=1,col=1)
lines(x,y2,lwd=3,lty=5,col=2)
lines(x,y3,lwd=3,lty=6,col=3)
points(x,y1,col=1,pch=16)
points(x,y2,col=1,pch=16)
points(x,y3,col=1,pch=16)
legend(leg[1],leg[2],legend=c('CVz1(1)','CVz1(2)','CVz1(3)'),lwd=c(3,3,3),lty=c(1,5,6),col=c(1,2,3))

z1=-.7646  #supply lower bound for z
z2=2.671   #supply upper bound for z
z <- seq(z1,z2,length=1000)
fz <- c(z,z)
y1 <- (0.00106+-0.038615*z)+(1.9744*sqrt(0.0001484268+(2*z*-0.00003870033)+((z^2)*0.0002145924)))
y2 <- (0.00106+-0.038615*z)-(1.9744*sqrt(0.0001484268+(2*z*-0.00003870033)+((z^2)*0.0002145924)))
fy <- c(y1,y2)
fline <- (0.00106+-0.038615*z)
plot(fz,fy,type='p',pch='.',font=2,font.lab=2,col=2,xlab='Moderator',ylab='Simple Slope',main='Confidence Bands')
lines(z,fline)
f0 <- array(0,c(1000))
lines(z,f0,col=8)
abline(v=-1.122,col=4,lty=2)
abline(v=0.7862,col=4,lty=2)

interact_plot(model, pred = VE, modx = Deprivation, x.label = "Violence Exposure", y.label = "Amygdala-OFC White Matter Connectivity", legend.main = "Social Deprivation",modx.values="plus-minus",plot.points = FALSE, color.class = c("gray54","steelblue4"),line.thickness = 2, rug = TRUE, rug.sides = "bl") + geom_line(linetype=1, size=2) + theme_classic() + theme(text=element_text(size = 16, family="serif")) 
## The color.class argument is deprecated. Please use 'colors' instead.

Threat as Moderator

TWO-WAY INTERACTION SIMPLE SLOPES OUTPUT

Your Input

X1 = -0.7646 X2 = 2.671 cv1 = -0.46 cv2 = 0.0384145 cv3 = 0.54 Intercept = 0.103768 X Slope = 0.020694 Z Slope = 0.00106 XZ Slope = -0.038615 df = 166 alpha = 0.05

Asymptotic (Co)variances

var(b0) 0.00003331 var(b1) 0.00018122 var(b2) 0.00014843 var(b3) 0.00021459 cov(b2,b0) 0.00000124 cov(b3,b1) -0.00006852

Region of Significance

Z at lower bound of region = -0.2291 Z at upper bound of region = 1.8545 (simple slopes are significant outside this region.)

Simple Intercepts and Slopes at Conditional Values of Z

At Z = cv1… simple intercept = 0.1033(0.008), t=12.953, p=0 simple slope = 0.0385(0.017), t=2.2596, p=0.0251 At Z = cv2… simple intercept = 0.1038(0.0058), t=17.9032, p=0 simple slope = 0.0192(0.0133), t=1.4469, p=0.1498 At Z = cv3… simple intercept = 0.1043(0.0088), t=11.8201, p=0 simple slope = -0.0002(0.013), t=-0.0121, p=0.9903

Simple Intercepts and Slopes at Region Boundaries

Lower Bound…
simple intercept = 0.1035(0.0064), t=16.2613, p=0 simple slope = 0.0295(0.015), t=1.9743, p=0.05 Upper Bound…
simple intercept = 0.1057(0.0234), t=4.5153, p=0 simple slope = -0.0509(0.0258), t=-1.9743, p=0.05

Points to Plot

Line for cv1: From {X=-0.7646, Y=0.0739} to {X=2.671, Y=0.206} Line for cv2: From {X=-0.7646, Y=0.0891} to {X=2.671, Y=0.1551} Line for cv3: From {X=-0.7646, Y=0.1045} to {X=2.671, Y=0.1039}

xx <- c(-0.7646,2.671)   #  <-- change to alter plot dims
yy <- c(0.0475,0.206)   #  <-- change to alter plot dims
leg <- c(-0.7646,0.0673)   #  <-- change to alter legend location
x <- c(-0.7646,2.671)   #  <-- x-coords for lines
y1 <- c(0.0739,0.206)
y2 <- c(0.0891,0.1551)
y3 <- c(0.1045,0.1039)
plot(xx,yy,type='n',font=2,font.lab=2,xlab='X',ylab='Y',main='MLR 2-Way Interaction Plot')
lines(x,y1,lwd=3,lty=1,col=1)
lines(x,y2,lwd=3,lty=5,col=2)
lines(x,y3,lwd=3,lty=6,col=3)
points(x,y1,col=1,pch=16)
points(x,y2,col=1,pch=16)
points(x,y3,col=1,pch=16)
legend(leg[1],leg[2],legend=c('CVz1(1)','CVz1(2)','CVz1(3)'),lwd=c(3,3,3),lty=c(1,5,6),col=c(1,2,3))

z1=-0.82  #supply lower bound for z
z2=2.17   #supply upper bound for z
z <- seq(z1,z2,length=1000)
fz <- c(z,z)
y1 <- (0.020694+-0.038615*z)+(1.9744*sqrt(0.0001812183+(2*z*-0.00006851916)+((z^2)*0.0002145924)))
y2 <- (0.020694+-0.038615*z)-(1.9744*sqrt(0.0001812183+(2*z*-0.00006851916)+((z^2)*0.0002145924)))
fy <- c(y1,y2)
fline <- (0.020694+-0.038615*z)
plot(fz,fy,type='p',pch='.',font=2,font.lab=2,col=2,xlab='Moderator',ylab='Simple Slope',main='Confidence Bands')
lines(z,fline)
f0 <- array(0,c(1000))
lines(z,f0,col=8)
abline(v=-0.2291,col=4,lty=2)
abline(v=1.8545,col=4,lty=2)

Testing to see if puberty score was higher in parent report.

t.test(clean.data$pubc_mean ~ clean.data$used_pubp)
## 
##  Welch Two Sample t-test
## 
## data:  clean.data$pubc_mean by clean.data$used_pubp
## t = -0.92773, df = 5.7577, p-value = 0.3908
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.5740279  0.2607511
## sample estimates:
## mean in group 0 mean in group 1 
##        3.240584        3.397222

Great, they’re not different.

Compare the full sample with the included sample

full_puberty = read_csv('/Users/leighgayle/Box Sync/FF_Demographics/FullSample_Puberty.csv')
## Parsed with column specification:
## cols(
##   ff_id = col_integer(),
##   puberty_score = col_double(),
##   used_parent = col_integer(),
##   id_short = col_integer()
## )
t.test(full_puberty$puberty_score, clean.data$pubc_mean)
## 
##  Welch Two Sample t-test
## 
## data:  full_puberty$puberty_score and clean.data$pubc_mean
## t = -0.14922, df = 394.16, p-value = 0.8815
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1217439  0.1045666
## sample estimates:
## mean of x mean of y 
##  3.237131  3.245719
t.test(full_puberty$puberty_score, clean.dataL$pubc_mean)
## 
##  Welch Two Sample t-test
## 
## data:  full_puberty$puberty_score and clean.dataL$pubc_mean
## t = -0.33286, df = 392.68, p-value = 0.7394
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.13291183  0.09442298
## sample estimates:
## mean of x mean of y 
##  3.237131  3.256375
full_age = read.csv('/Users/leighgayle/Box Sync/FF_Demographics/Teen.age.csv')

t.test(full_age$Age_Years, clean.data$AgeYears)
## 
##  Welch Two Sample t-test
## 
## data:  full_age$Age_Years and clean.data$AgeYears
## t = 0.31929, df = 398.04, p-value = 0.7497
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08601324  0.11936908
## sample estimates:
## mean of x mean of y 
##  15.86965  15.85297
t.test(full_age$Age_Years, clean.dataL$AgeYears)
## 
##  Welch Two Sample t-test
## 
## data:  full_age$Age_Years and clean.dataL$AgeYears
## t = 0.2993, df = 396.31, p-value = 0.7649
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08753764  0.11897717
## sample estimates:
## mean of x mean of y 
##  15.86965  15.85393

Making some tables of the non-significant regressions

RAmy_BA10_reg = lm(RAmy_BA10 ~ VE + Deprivation + ThreatDepInt + RAmy_BA47, data = clean.data)
apa.reg.table(RAmy_BA10_reg, filename = "RAmy_BA10_Table.doc", table.number = 5)
## 
## 
## Table 5 
## 
## Regression results using RAmy_BA10 as the criterion
##  
## 
##     Predictor      b      b_95%_CI  beta   beta_95%_CI sr2  sr2_95%_CI
##   (Intercept) 0.03**  [0.02, 0.04]                                    
##            VE   0.01 [-0.01, 0.02]  0.07 [-0.09, 0.24] .00 [-.01, .02]
##   Deprivation   0.00 [-0.01, 0.02]  0.04 [-0.13, 0.21] .00 [-.01, .01]
##  ThreatDepInt  -0.02 [-0.03, 0.00] -0.16 [-0.32, 0.01] .02 [-.02, .05]
##     RAmy_BA47 0.15**  [0.07, 0.23]  0.27  [0.13, 0.41] .07  [.00, .14]
##                                                                       
##                                                                       
##                                                                       
##      r             Fit
##                       
##    .02                
##    .01                
##  -.15*                
##  .30**                
##            R2 = .107**
##        95% CI[.02,.18]
##                       
## 
## Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
## b represents unstandardized regression weights. beta indicates the standardized regression weights. 
## sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
## * indicates p < .05. ** indicates p < .01.
## 
lm.beta(RAmy_BA10_reg)
##           VE  Deprivation ThreatDepInt    RAmy_BA47 
##   0.07072681   0.03926044  -0.15871285   0.27121381
summary(RAmy_BA10_reg)
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE + Deprivation + ThreatDepInt + RAmy_BA47, 
##     data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.067036 -0.030903 -0.008152  0.018446  0.120935 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.034716   0.005148   6.743 2.09e-10 ***
## VE            0.005491   0.006488   0.846 0.398551    
## Deprivation   0.003235   0.007216   0.448 0.654496    
## ThreatDepInt -0.015010   0.007951  -1.888 0.060683 .  
## RAmy_BA47     0.149326   0.039804   3.752 0.000238 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03958 on 178 degrees of freedom
## Multiple R-squared:  0.1067, Adjusted R-squared:  0.08667 
## F-statistic: 5.318 on 4 and 178 DF,  p-value: 0.0004551
LAmy_BA10_reg = lm(LAmy_BA10 ~ VE + Deprivation + ThreatDepInt + LAmy_BA11, data = clean.dataL)

lm.beta(LAmy_BA10_reg)
##           VE  Deprivation ThreatDepInt    LAmy_BA11 
##   0.02637087   0.01286257  -0.06424797   0.61032230
summary(LAmy_BA10_reg)
## 
## Call:
## lm(formula = LAmy_BA10 ~ VE + Deprivation + ThreatDepInt + LAmy_BA11, 
##     data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.027573 -0.004216 -0.002333  0.000593  0.055848 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.0030810  0.0012229   2.519   0.0126 *  
## VE            0.0007613  0.0020454   0.372   0.7102    
## Deprivation   0.0004249  0.0024110   0.176   0.8603    
## ThreatDepInt -0.0022878  0.0024140  -0.948   0.3445    
## LAmy_BA11     0.3900709  0.0378926  10.294   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01236 on 178 degrees of freedom
## Multiple R-squared:  0.3807, Adjusted R-squared:  0.3668 
## F-statistic: 27.35 on 4 and 178 DF,  p-value: < 2.2e-16
LAmy_BA11_reg = lm(LAmy_BA11 ~ VE + Deprivation + ThreatDepInt + LAmy_BA10, data = clean.dataL)

lm.beta(LAmy_BA11_reg)
##           VE  Deprivation ThreatDepInt    LAmy_BA10 
##  -0.04180465   0.01131408  -0.01831956   0.61143137
summary(LAmy_BA11_reg)
## 
## Call:
## lm(formula = LAmy_BA11 ~ VE + Deprivation + ThreatDepInt + LAmy_BA10, 
##     data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.046114 -0.009221 -0.005651  0.004268  0.083652 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.0095433  0.0018130   5.264 4.03e-07 ***
## VE           -0.0018883  0.0032013  -0.590    0.556    
## Deprivation   0.0005848  0.0037758   0.155    0.877    
## ThreatDepInt -0.0010207  0.0037892  -0.269    0.788    
## LAmy_BA10     0.9566727  0.0929340  10.294  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01935 on 178 degrees of freedom
## Multiple R-squared:  0.3795, Adjusted R-squared:  0.3656 
## F-statistic: 27.22 on 4 and 178 DF,  p-value: < 2.2e-16

Checking to see how correlated threat and dep are:

cor.test(clean.data$VE, clean.data$Deprivation)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$VE and clean.data$Deprivation
## t = 7.6865, df = 181, p-value = 9.272e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3782395 0.5980962
## sample estimates:
##       cor 
## 0.4960782
fit_cor = lm(RAmy_BA47 ~ VE + Deprivation, data = clean.data)
vif(fit_cor)
##          VE Deprivation 
##    1.326425    1.326425
par(family = 'serif')
plot(clean.data$VE, clean.data$Deprivation, frame.plot = FALSE,  main=NULL, cex.lab = 1.5, font.lab=2, ylab = "Deprivation", xlab = "Violence Exposure", pch=16)

Analyses not used in final paper.

Incorporation of Psychopathology Data

Note: This is not in the final manuscript.

We’ve got a two factor (anxiety and depression) score and a one factor (internalizing) score created by T.H. that I’ll use.

summary(lm(Internalizing ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, data = clean.data))
## 
## Call:
## lm(formula = Internalizing ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + 
##     RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + 
##     LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + 
##     LAmy_BA47, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73954 -0.31372 -0.08247  0.27263  1.09336 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.06597    0.08908  -0.741    0.460
## RAmy_BA47     0.07627    0.49878   0.153    0.879
## RAmy_BA10     0.16791    1.00735   0.167    0.868
## RAmy_BA11    -0.03244    0.63159  -0.051    0.959
## RAmy_BA24     7.56775    8.76484   0.863    0.389
## RAmy_BA25     0.47272    0.51327   0.921    0.358
## RAmy_BA32    -3.09759    3.48772  -0.888    0.376
## RAmy_BA9     -1.39669    3.86294  -0.362    0.718
## LAmy_BA10     4.09284    2.57346   1.590    0.114
## LAmy_BA11    -1.73328    1.86575  -0.929    0.354
## LAmy_BA9     26.82037   19.12413   1.402    0.163
## LAmy_BA24    -4.21244    6.03390  -0.698    0.486
## LAmy_BA25     0.23894    0.56981   0.419    0.676
## LAmy_BA32   -15.23724   15.37100  -0.991    0.323
## LAmy_BA47     0.64751    1.73676   0.373    0.710
## 
## Residual standard error: 0.4248 on 167 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.05372,    Adjusted R-squared:  -0.02561 
## F-statistic: 0.6772 on 14 and 167 DF,  p-value: 0.7944
summary(lm(Anxiety ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, data = clean.data))
## 
## Call:
## lm(formula = Anxiety ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + 
##     RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + 
##     LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, 
##     data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.22937 -0.09263 -0.02237  0.08178  0.34108 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01758    0.02719  -0.647    0.519
## RAmy_BA47    0.01796    0.15222   0.118    0.906
## RAmy_BA10    0.05488    0.30742   0.179    0.859
## RAmy_BA11   -0.01699    0.19275  -0.088    0.930
## RAmy_BA24    2.29422    2.67486   0.858    0.392
## RAmy_BA25    0.14095    0.15664   0.900    0.369
## RAmy_BA32   -0.85373    1.06438  -0.802    0.424
## RAmy_BA9    -0.56197    1.17889  -0.477    0.634
## LAmy_BA10    1.20849    0.78537   1.539    0.126
## LAmy_BA11   -0.49351    0.56939  -0.867    0.387
## LAmy_BA9     8.31291    5.83631   1.424    0.156
## LAmy_BA24   -1.28912    1.84143  -0.700    0.485
## LAmy_BA25    0.05732    0.17389   0.330    0.742
## LAmy_BA32   -4.21521    4.69093  -0.899    0.370
## LAmy_BA47    0.17774    0.53002   0.335    0.738
## 
## Residual standard error: 0.1297 on 167 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.05122,    Adjusted R-squared:  -0.02832 
## F-statistic: 0.6439 on 14 and 167 DF,  p-value: 0.8251
summary(lm(Depression ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, data = clean.data))
## 
## Call:
## lm(formula = Depression ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + 
##     RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + 
##     LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + 
##     LAmy_BA47, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73036 -0.31893 -0.08126  0.27474  1.08018 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.06907    0.08864  -0.779    0.437
## RAmy_BA47     0.08267    0.49631   0.167    0.868
## RAmy_BA10     0.15963    1.00237   0.159    0.874
## RAmy_BA11    -0.01718    0.62847  -0.027    0.978
## RAmy_BA24     7.53709    8.72147   0.864    0.389
## RAmy_BA25     0.47742    0.51073   0.935    0.351
## RAmy_BA32    -3.16967    3.47046  -0.913    0.362
## RAmy_BA9     -1.25038    3.84382  -0.325    0.745
## LAmy_BA10     4.11192    2.56073   1.606    0.110
## LAmy_BA11    -1.76314    1.85652  -0.950    0.344
## LAmy_BA9     26.42121   19.02950   1.388    0.167
## LAmy_BA24    -4.17902    6.00404  -0.696    0.487
## LAmy_BA25     0.25368    0.56699   0.447    0.655
## LAmy_BA32   -15.53839   15.29494  -1.016    0.311
## LAmy_BA47     0.66632    1.72816   0.386    0.700
## 
## Residual standard error: 0.4227 on 167 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.05452,    Adjusted R-squared:  -0.02474 
## F-statistic: 0.6878 on 14 and 167 DF,  p-value: 0.7842
summary(lm(ADHD_Both_CurrentSx_012 ~ Ramy_0035 , data = data))
## 
## Call:
## lm(formula = ADHD_Both_CurrentSx_012 ~ Ramy_0035, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.155 -4.146 -2.150  1.850 29.853 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 4.143216   0.869190   4.767 4.28e-06 ***
## Ramy_0035   0.003958   0.820021   0.005    0.996    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.787 on 155 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  1.503e-07,  Adjusted R-squared:  -0.006451 
## F-statistic: 2.329e-05 on 1 and 155 DF,  p-value: 0.9962

Plot amygdala associations

plot(clean.data$RAmy_BA47, clean.data$Ramy_0035)

plot(clean.dataL$LAmy_BA10, clean.dataL$Lamy_0035)

plot(data$DepCompc, data$LAmy_BA10)

plot(clean.dataL$DepCompc, clean.dataL$LAmy_BA10)

Relate to Hab Data

cor.test(clean.data$Ramy_Hab_Con0041, clean.data$RAmy_BA47)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_Hab_Con0041 and clean.data$RAmy_BA47
## t = 0.46238, df = 147, p-value = 0.6445
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1234475  0.1976974
## sample estimates:
##        cor 
## 0.03810896
cor.test(clean.data$Ramy_Hab_Con0035, clean.data$RAmy_BA47)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_Hab_Con0035 and clean.data$RAmy_BA47
## t = 0.36895, df = 147, p-value = 0.7127
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1310248  0.1902853
## sample estimates:
##        cor 
## 0.03041602
cor.test(clean.data$Ramy_Hab_Con0027, clean.data$RAmy_BA47)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_Hab_Con0027 and clean.data$RAmy_BA47
## t = 0.3151, df = 147, p-value = 0.7531
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1353849  0.1860035
## sample estimates:
##        cor 
## 0.02598065

Nothing for RAmy_BA47 and Hab.

cor.test(clean.data$Ramy_Hab_Con0041, clean.data$RAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_Hab_Con0041 and clean.data$RAmy_BA10
## t = 1.0666, df = 147, p-value = 0.2879
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07421622  0.24497752
## sample estimates:
##       cor 
## 0.0876295
cor.test(clean.data$Ramy_Hab_Con0035, clean.data$RAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_Hab_Con0035 and clean.data$RAmy_BA10
## t = -0.15172, df = 147, p-value = 0.8796
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1729648  0.1485860
## sample estimates:
##        cor 
## -0.0125129
cor.test(clean.data$Ramy_Hab_Con0027, clean.data$RAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$Ramy_Hab_Con0027 and clean.data$RAmy_BA10
## t = 1.5747, df = 147, p-value = 0.1175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03267615  0.28372427
## sample estimates:
##       cor 
## 0.1288006

RAmy_BA10 is more related to Hab, but still not statistically significant.

cor.test(clean.dataL$Lamy_Hab_Con0041, clean.dataL$LAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_Hab_Con0041 and clean.dataL$LAmy_BA10
## t = 0.33581, df = 148, p-value = 0.7375
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1332578  0.1870274
## sample estimates:
##        cor 
## 0.02759297
cor.test(clean.dataL$Lamy_Hab_Con0035, clean.dataL$LAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_Hab_Con0035 and clean.dataL$LAmy_BA10
## t = -0.098906, df = 148, p-value = 0.9213
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1681722  0.1523303
## sample estimates:
##          cor 
## -0.008129737
cor.test(clean.dataL$Lamy_Hab_Con0027, clean.dataL$LAmy_BA10)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_Hab_Con0027 and clean.dataL$LAmy_BA10
## t = 0.66792, df = 148, p-value = 0.5052
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1063763  0.2132082
## sample estimates:
##        cor 
## 0.05481981

Nothing for LAmy_BA10 and Hab.

cor.test(clean.dataL$Lamy_Hab_Con0041, clean.dataL$LAmy_BA11)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_Hab_Con0041 and clean.dataL$LAmy_BA11
## t = 0.48316, df = 148, p-value = 0.6297
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1213489  0.1986823
## sample estimates:
##        cor 
## 0.03968437
cor.test(clean.dataL$Lamy_Hab_Con0035, clean.dataL$LAmy_BA11)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_Hab_Con0035 and clean.dataL$LAmy_BA11
## t = -0.53628, df = 148, p-value = 0.5926
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2028685  0.1170489
## sample estimates:
##         cor 
## -0.04403877
cor.test(clean.dataL$Lamy_Hab_Con0027, clean.dataL$LAmy_BA11)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.dataL$Lamy_Hab_Con0027 and clean.dataL$LAmy_BA11
## t = 1.4096, df = 148, p-value = 0.1608
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.04601397  0.27037071
## sample estimates:
##       cor 
## 0.1150963

Also nothing for Hab and LAmy_BA11.

Some additional analyses that people may be interested in.

I suppose people will want me to look at all of the potential regions, so here it goes. I just want to document here though that this occurred after my initial exploration of the original significant regions.

# Right Hemisphere
summary(lm(RAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10324 -0.05575 -0.01503  0.04276  0.20014 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.112240   0.057172   1.963   0.0512 .
## VE             -0.006036   0.011890  -0.508   0.6123  
## Deprivation    -0.005611   0.013157  -0.426   0.6703  
## EthnoRace_3cat -0.005633   0.007657  -0.736   0.4629  
## Gender_1F_2M   -0.023729   0.013863  -1.712   0.0887 .
## Internalizing   0.007912   0.013318   0.594   0.5532  
## pubc_mean       0.007061   0.011628   0.607   0.5445  
## VE:Deprivation  0.006525   0.014078   0.463   0.6436  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07092 on 175 degrees of freedom
## Multiple R-squared:  0.0557, Adjusted R-squared:  0.01792 
## F-statistic: 1.475 on 7 and 175 DF,  p-value: 0.1791
summary(lm(RAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.004167 -0.002211 -0.001269  0.000072  0.035604 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.0049901  0.0036148   1.380    0.169
## VE              0.0006747  0.0007518   0.898    0.371
## Deprivation     0.0005087  0.0008319   0.611    0.542
## EthnoRace_3cat -0.0001225  0.0004841  -0.253    0.801
## Gender_1F_2M   -0.0003722  0.0008765  -0.425    0.672
## Internalizing   0.0002925  0.0008421   0.347    0.729
## pubc_mean      -0.0003999  0.0007352  -0.544    0.587
## VE:Deprivation -0.0007434  0.0008901  -0.835    0.405
## 
## Residual standard error: 0.004484 on 175 degrees of freedom
## Multiple R-squared:  0.01482,    Adjusted R-squared:  -0.02458 
## F-statistic: 0.3761 on 7 and 175 DF,  p-value: 0.9154
summary(lm(RAmy_BA11 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA11 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.08485 -0.05640 -0.01547  0.04440  0.22191 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.078831   0.055926   1.410    0.160
## VE             -0.015480   0.011631  -1.331    0.185
## Deprivation     0.004850   0.012871   0.377    0.707
## EthnoRace_3cat  0.002774   0.007490   0.370    0.712
## Gender_1F_2M    0.007687   0.013561   0.567    0.572
## Internalizing   0.001974   0.013028   0.151    0.880
## pubc_mean      -0.005084   0.011374  -0.447    0.655
## VE:Deprivation -0.011100   0.013772  -0.806    0.421
## 
## Residual standard error: 0.06938 on 175 degrees of freedom
## Multiple R-squared:  0.02421,    Adjusted R-squared:  -0.01482 
## F-statistic: 0.6203 on 7 and 175 DF,  p-value: 0.7387
summary(lm(RAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.011832 -0.007461 -0.004832  0.000334  0.103865 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.0101414  0.0125711   0.807    0.421
## VE              0.0026951  0.0026144   1.031    0.304
## Deprivation    -0.0005803  0.0028931  -0.201    0.841
## EthnoRace_3cat -0.0006412  0.0016837  -0.381    0.704
## Gender_1F_2M   -0.0009671  0.0030483  -0.317    0.751
## Internalizing  -0.0020059  0.0029285  -0.685    0.494
## pubc_mean       0.0005674  0.0025567   0.222    0.825
## VE:Deprivation -0.0038126  0.0030956  -1.232    0.220
## 
## Residual standard error: 0.01559 on 175 degrees of freedom
## Multiple R-squared:  0.01746,    Adjusted R-squared:  -0.02184 
## F-statistic: 0.4443 on 7 and 175 DF,  p-value: 0.873
summary(lm(RAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.data))
## 
## Call:
## lm(formula = RAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + 
##     Internalizing + pubc_mean, data = clean.data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.014370 -0.007441 -0.005085  0.001558  0.133670 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     0.0101125  0.0128203   0.789   0.4313  
## VE              0.0055048  0.0026663   2.065   0.0404 *
## Deprivation    -0.0019388  0.0029504  -0.657   0.5120  
## EthnoRace_3cat -0.0007912  0.0017170  -0.461   0.6455  
## Gender_1F_2M   -0.0007116  0.0031087  -0.229   0.8192  
## Internalizing  -0.0005275  0.0029865  -0.177   0.8600  
## pubc_mean       0.0005723  0.0026074   0.219   0.8265  
## VE:Deprivation -0.0033110  0.0031569  -1.049   0.2957  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0159 on 175 degrees of freedom
## Multiple R-squared:  0.02713,    Adjusted R-squared:  -0.01178 
## F-statistic: 0.6973 on 7 and 175 DF,  p-value: 0.6743
# Left Hemisphere
summary(lm(LAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.09234 -0.05060 -0.01075  0.04134  0.20020 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.076067   0.051168   1.487    0.139
## VE              0.004024   0.010872   0.370    0.712
## Deprivation    -0.002226   0.012621  -0.176    0.860
## EthnoRace_3cat  0.005667   0.006955   0.815    0.416
## Gender_1F_2M   -0.012535   0.012555  -0.998    0.319
## Internalizing   0.006765   0.011909   0.568    0.571
## pubc_mean       0.003700   0.010573   0.350    0.727
## VE:Deprivation -0.001429   0.012502  -0.114    0.909
## 
## Residual standard error: 0.06372 on 174 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.02518,    Adjusted R-squared:  -0.01404 
## F-statistic: 0.642 on 7 and 174 DF,  p-value: 0.7207
summary(lm(LAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.004792 -0.002642 -0.001426  0.000459  0.039476 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.0027783  0.0043438   0.640    0.523
## VE              0.0012933  0.0009229   1.401    0.163
## Deprivation    -0.0017135  0.0010714  -1.599    0.112
## EthnoRace_3cat -0.0002864  0.0005904  -0.485    0.628
## Gender_1F_2M   -0.0004564  0.0010659  -0.428    0.669
## Internalizing  -0.0006608  0.0010110  -0.654    0.514
## pubc_mean       0.0005869  0.0008975   0.654    0.514
## VE:Deprivation -0.0006838  0.0010614  -0.644    0.520
## 
## Residual standard error: 0.005409 on 174 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0354, Adjusted R-squared:  -0.003406 
## F-statistic: 0.9122 on 7 and 174 DF,  p-value: 0.4983
summary(lm(LAmy_BA47 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA47 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.027171 -0.016695 -0.009790  0.009257  0.093960 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.0204217  0.0203324   1.004    0.317
## VE             -0.0046334  0.0043201  -1.073    0.285
## Deprivation     0.0047898  0.0050151   0.955    0.341
## EthnoRace_3cat  0.0008155  0.0027636   0.295    0.768
## Gender_1F_2M    0.0031838  0.0049891   0.638    0.524
## Internalizing   0.0029887  0.0047322   0.632    0.529
## pubc_mean      -0.0012774  0.0042012  -0.304    0.761
## VE:Deprivation -0.0014029  0.0049680  -0.282    0.778
## 
## Residual standard error: 0.02532 on 174 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.01627,    Adjusted R-squared:  -0.02331 
## F-statistic: 0.411 on 7 and 174 DF,  p-value: 0.8946
summary(lm(LAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat + 
##     Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0023513 -0.0012279 -0.0006189  0.0001051  0.0158594 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     3.343e-03  2.034e-03   1.643   0.1021  
## VE              8.515e-04  4.322e-04   1.970   0.0504 .
## Deprivation    -6.163e-04  5.017e-04  -1.228   0.2210  
## EthnoRace_3cat -1.745e-05  2.765e-04  -0.063   0.9497  
## Gender_1F_2M   -9.220e-04  4.991e-04  -1.847   0.0664 .
## Internalizing  -2.466e-04  4.734e-04  -0.521   0.6031  
## pubc_mean      -1.429e-04  4.203e-04  -0.340   0.7343  
## VE:Deprivation -4.826e-04  4.970e-04  -0.971   0.3328  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.002533 on 174 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.04921,    Adjusted R-squared:  0.01096 
## F-statistic: 1.287 on 7 and 174 DF,  p-value: 0.2595
summary(lm(LAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M  + Internalizing + pubc_mean, data=clean.dataL))
## 
## Call:
## lm(formula = LAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + 
##     Internalizing + pubc_mean, data = clean.dataL)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.002611 -0.001399 -0.000675  0.000389  0.041338 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)    -0.0045317  0.0030795  -1.472    0.143
## VE              0.0001276  0.0006543   0.195    0.846
## Deprivation    -0.0001965  0.0007596  -0.259    0.796
## EthnoRace_3cat  0.0004712  0.0004186   1.126    0.262
## Gender_1F_2M    0.0010335  0.0007556   1.368    0.173
## Internalizing   0.0005593  0.0007167   0.780    0.436
## pubc_mean       0.0010284  0.0006363   1.616    0.108
## VE:Deprivation -0.0003785  0.0007525  -0.503    0.616
## 
## Residual standard error: 0.003835 on 174 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.02838,    Adjusted R-squared:  -0.01071 
## F-statistic: 0.726 on 7 and 174 DF,  p-value: 0.6502

A foray into externalizing disorders

cor.test(clean.data$RAmy_BA47, clean.data$ADHD_Both_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA47 and clean.data$ADHD_Both_CurrentSx_012
## t = 0.60249, df = 181, p-value = 0.5476
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1009741  0.1885707
## sample estimates:
##        cor 
## 0.04473782
cor.test(clean.data$RAmy_BA47, clean.data$ADHD_INT_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA47 and clean.data$ADHD_INT_CurrentSx_012
## t = 0.28697, df = 181, p-value = 0.7745
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1241150  0.1658692
## sample estimates:
##        cor 
## 0.02132564
cor.test(clean.data$RAmy_BA47, clean.data$ADHD_HYP_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA47 and clean.data$ADHD_HYP_CurrentSx_012
## t = 0.87975, df = 181, p-value = 0.3802
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.08056734  0.20833660
## sample estimates:
##       cor 
## 0.0652519
cor.test(clean.data$RAmy_BA47, clean.data$ODD_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA47 and clean.data$ODD_CurrentSx_012
## t = -0.10379, df = 181, p-value = 0.9175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1526001  0.1374963
## sample estimates:
##          cor 
## -0.007714247
summary(lm(ADHD_HYP_CurrentSx_012 ~ VE * Deprivation, data = clean.data))
## 
## Call:
## lm(formula = ADHD_HYP_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6237 -1.5579 -0.8720  0.1638 16.6204 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.5598     0.2516   6.199 3.81e-09 ***
## VE               1.9371     0.5311   3.647 0.000348 ***
## Deprivation     -0.4752     0.5869  -0.810 0.419195    
## VE:Deprivation  -1.3755     0.6387  -2.154 0.032599 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.24 on 179 degrees of freedom
## Multiple R-squared:  0.07852,    Adjusted R-squared:  0.06308 
## F-statistic: 5.084 on 3 and 179 DF,  p-value: 0.002113
summary(lm(ADHD_INT_CurrentSx_012 ~ VE * Deprivation, data = clean.data))
## 
## Call:
## lm(formula = ADHD_INT_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8458 -2.7717 -2.1251  0.8705 15.2486 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      2.9170     0.3555   8.204  4.4e-14 ***
## VE               0.5163     0.7506   0.688    0.492    
## Deprivation     -0.4611     0.8293  -0.556    0.579    
## VE:Deprivation  -0.6568     0.9025  -0.728    0.468    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.578 on 179 degrees of freedom
## Multiple R-squared:  0.007162,   Adjusted R-squared:  -0.009478 
## F-statistic: 0.4304 on 3 and 179 DF,  p-value: 0.7315
summary(lm(ADHD_Both_CurrentSx_012 ~ VE * Deprivation, data = clean.data))
## 
## Call:
## lm(formula = ADHD_Both_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4694 -3.7747 -2.5809  0.8904 30.7562 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      4.4768     0.5436   8.236 3.63e-14 ***
## VE               2.4534     1.1475   2.138   0.0339 *  
## Deprivation     -0.9363     1.2679  -0.738   0.4612    
## VE:Deprivation  -2.0323     1.3797  -1.473   0.1425    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.999 on 179 degrees of freedom
## Multiple R-squared:  0.03171,    Adjusted R-squared:  0.01548 
## F-statistic: 1.954 on 3 and 179 DF,  p-value: 0.1226
summary(lm(ODD_CurrentSx_012 ~ VE * Deprivation, data = clean.data))
## 
## Call:
## lm(formula = ODD_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6158 -1.5080 -1.1038 -0.1268 12.4333 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.472820   0.217050   6.786 1.63e-10 ***
## VE              0.857791   0.458196   1.872   0.0628 .  
## Deprivation    -0.005558   0.506286  -0.011   0.9913    
## VE:Deprivation -0.677161   0.550937  -1.229   0.2206    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.795 on 179 degrees of freedom
## Multiple R-squared:  0.02387,    Adjusted R-squared:  0.00751 
## F-statistic: 1.459 on 3 and 179 DF,  p-value: 0.2273
summary(lm(ADHD_HYP_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))
## 
## Call:
## lm(formula = ADHD_HYP_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + 
##     Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2493 -1.7059 -0.8878 -0.1826 17.1588 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    1.8952     0.8451   2.243   0.0262 *
## RAmy_BA47      3.1544     3.4517   0.914   0.3620  
## RAmy_BA10     -2.5025     6.3840  -0.392   0.6955  
## Gender_0F_1M   0.9295     0.4967   1.872   0.0629 .
## EthnoRace_C   -1.7542     0.9487  -1.849   0.0661 .
## EthnoRace_AA  -1.1475     0.7478  -1.535   0.1267  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.309 on 177 degrees of freedom
## Multiple R-squared:  0.04963,    Adjusted R-squared:  0.02279 
## F-statistic: 1.849 on 5 and 177 DF,  p-value: 0.1057
summary(lm(ADHD_INT_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))
## 
## Call:
## lm(formula = ADHD_INT_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + 
##     Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1019 -2.6464 -1.5444  0.7104 14.8818 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   2.12540    1.14822   1.851   0.0658 .
## RAmy_BA47    -1.64082    4.68988  -0.350   0.7269  
## RAmy_BA10    14.75289    8.67398   1.701   0.0907 .
## Gender_0F_1M  1.63418    0.67481   2.422   0.0165 *
## EthnoRace_C   0.05433    1.28907   0.042   0.9664  
## EthnoRace_AA -0.82420    1.01601  -0.811   0.4183  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.496 on 177 degrees of freedom
## Multiple R-squared:  0.05336,    Adjusted R-squared:  0.02662 
## F-statistic: 1.995 on 5 and 177 DF,  p-value: 0.08148
summary(lm(ADHD_Both_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))
## 
## Call:
## lm(formula = ADHD_Both_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + 
##     Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.2173 -3.5825 -2.3233  0.1034 29.9354 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     4.021      1.779   2.261   0.0250 *
## RAmy_BA47       1.514      7.265   0.208   0.8352  
## RAmy_BA10      12.250     13.436   0.912   0.3631  
## Gender_0F_1M    2.564      1.045   2.453   0.0152 *
## EthnoRace_C    -1.700      1.997  -0.851   0.3957  
## EthnoRace_AA   -1.972      1.574  -1.253   0.2119  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.964 on 177 degrees of freedom
## Multiple R-squared:  0.05217,    Adjusted R-squared:  0.02539 
## F-statistic: 1.948 on 5 and 177 DF,  p-value: 0.08861
summary(lm(ODD_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))
## 
## Call:
## lm(formula = ODD_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + 
##     EthnoRace_C + EthnoRace_AA, data = clean.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9031 -1.4834 -1.2628 -0.1831 12.7625 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    1.6661     0.7240   2.301   0.0225 *
## RAmy_BA47      0.2990     2.9570   0.101   0.9196  
## RAmy_BA10     -3.2690     5.4691  -0.598   0.5508  
## Gender_0F_1M   0.2240     0.4255   0.526   0.5992  
## EthnoRace_C   -0.6228     0.8128  -0.766   0.4446  
## EthnoRace_AA  -0.1922     0.6406  -0.300   0.7645  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.835 on 177 degrees of freedom
## Multiple R-squared:  0.007149,   Adjusted R-squared:  -0.0209 
## F-statistic: 0.2549 on 5 and 177 DF,  p-value: 0.9369
cor.test(clean.data$RAmy_BA10, clean.data$ADHD_Both_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA10 and clean.data$ADHD_Both_CurrentSx_012
## t = 0.92146, df = 181, p-value = 0.358
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07749358  0.21129351
## sample estimates:
##        cor 
## 0.06833117
cor.test(clean.data$RAmy_BA10, clean.data$ADHD_INT_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA10 and clean.data$ADHD_INT_CurrentSx_012
## t = 1.4772, df = 181, p-value = 0.1414
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03648892  0.25024021
## sample estimates:
##       cor 
## 0.1091454
cor.test(clean.data$RAmy_BA10, clean.data$ADHD_HYP_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA10 and clean.data$ADHD_HYP_CurrentSx_012
## t = -0.061647, df = 181, p-value = 0.9509
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1495394  0.1405679
## sample estimates:
##          cor 
## -0.004582154
cor.test(clean.data$RAmy_BA10, clean.data$ODD_CurrentSx_012)
## 
##  Pearson's product-moment correlation
## 
## data:  clean.data$RAmy_BA10 and clean.data$ODD_CurrentSx_012
## t = -0.50025, df = 181, p-value = 0.6175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1812371  0.1084840
## sample estimates:
##         cor 
## -0.03715734

Ensuring that the effects are not due to medications.

Subjects 10003, 10023, 10154, and 10184 are on antipsychotics or anticonvulsants. I think we’ll exclude these folks and see what we’ve got.

no_antipsych_data = clean.data[-c(3,22,126,150), ]
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data=no_antipsych_data))
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data = no_antipsych_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.12882 -0.05300 -0.01711  0.05242  0.21397 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.072599   0.046072   1.576  0.11695    
## VE              0.004606   0.012326   0.374  0.70910    
## Deprivation     0.018697   0.013724   1.362  0.17492    
## EthnoRace_C     0.016371   0.021456   0.763  0.44652    
## EthnoRace_AA   -0.019830   0.016895  -1.174  0.24216    
## Gender_0F_1M    0.005553   0.014295   0.388  0.69816    
## RAmy_BA10       0.527048   0.139088   3.789  0.00021 ***
## Internalizing   0.008377   0.013934   0.601  0.54853    
## pubc_mean       0.004571   0.012086   0.378  0.70572    
## VE:Deprivation -0.035318   0.015036  -2.349  0.01999 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07213 on 169 degrees of freedom
## Multiple R-squared:  0.1282, Adjusted R-squared:  0.08182 
## F-statistic: 2.762 on 9 and 169 DF,  p-value: 0.00486
cor.test(no_antipsych_data$RAmy_BA47, no_antipsych_data$ThreatCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_antipsych_data$RAmy_BA47 and no_antipsych_data$ThreatCompc
## t = -0.25291, df = 177, p-value = 0.8006
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1652183  0.1280226
## sample estimates:
##         cor 
## -0.01900655
cor.test(no_antipsych_data$RAmy_BA47, no_antipsych_data$DepCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_antipsych_data$RAmy_BA47 and no_antipsych_data$DepCompc
## t = 0.29686, df = 177, p-value = 0.7669
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1247727  0.1684290
## sample estimates:
##        cor 
## 0.02230779
summary(lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data = no_antipsych_data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, 
##     data = no_antipsych_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56282 -0.37431  0.02869  0.35142  1.93076 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.21644    0.30664   3.967 0.000114 ***
## RAmy_BA47     -2.75789    0.70081  -3.935 0.000128 ***
## Internalizing  0.17906    0.12654   1.415 0.159207    
## pubc_mean     -0.03204    0.09120  -0.351 0.725856    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6444 on 145 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.1046, Adjusted R-squared:  0.0861 
## F-statistic: 5.648 on 3 and 145 DF,  p-value: 0.001093
summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data=no_antipsych_data))
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data = no_antipsych_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.05744 -0.02956 -0.00912  0.01785  0.11939 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     2.522e-02  2.456e-02   1.027  0.30602    
## VE              1.165e-03  6.546e-03   0.178  0.85892    
## Deprivation     7.047e-05  7.327e-03   0.010  0.99234    
## EthnoRace_C    -7.302e-03  1.140e-02  -0.641  0.52263    
## EthnoRace_AA    1.579e-02  8.925e-03   1.769  0.07867 .  
## Gender_0F_1M    1.803e-04  7.593e-03   0.024  0.98108    
## RAmy_BA47       1.486e-01  3.921e-02   3.789  0.00021 ***
## Internalizing   3.354e-03  7.402e-03   0.453  0.65107    
## pubc_mean      -8.503e-04  6.419e-03  -0.132  0.89478    
## VE:Deprivation -8.305e-03  8.088e-03  -1.027  0.30594    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0383 on 169 degrees of freedom
## Multiple R-squared:  0.1408, Adjusted R-squared:  0.09509 
## F-statistic: 3.078 on 9 and 169 DF,  p-value: 0.001912
cor.test(no_antipsych_data$RAmy_BA10, no_antipsych_data$ThreatCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_antipsych_data$RAmy_BA10 and no_antipsych_data$ThreatCompc
## t = 0.2451, df = 177, p-value = 0.8067
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1285999  0.1646473
## sample estimates:
##        cor 
## 0.01841986
cor.test(no_antipsych_data$RAmy_BA10, no_antipsych_data$DepCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_antipsych_data$RAmy_BA10 and no_antipsych_data$DepCompc
## t = 0.068653, df = 177, p-value = 0.9453
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1416193  0.1517176
## sample estimates:
##         cor 
## 0.005160188
summary(lm(Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, data = no_antipsych_data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, 
##     data = no_antipsych_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88289 -0.33502  0.00632  0.33461  1.93854 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.14418    0.31406   3.643 0.000374 ***
## RAmy_BA10     -3.80083    1.34663  -2.822 0.005435 ** 
## Internalizing  0.15940    0.12942   1.232 0.220066    
## pubc_mean     -0.04300    0.09345  -0.460 0.646067    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.66 on 145 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.06061,    Adjusted R-squared:  0.04117 
## F-statistic: 3.118 on 3 and 145 DF,  p-value: 0.02804
no_anxiety_dep_med_data = clean.data[-c(3,22,126,138,150,162), ]
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data=no_anxiety_dep_med_data))
## 
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data = no_anxiety_dep_med_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.13009 -0.05361 -0.01864  0.05364  0.21577 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.065121   0.046831   1.391 0.166215    
## VE              0.004133   0.012406   0.333 0.739476    
## Deprivation     0.021172   0.014104   1.501 0.135211    
## EthnoRace_C     0.015304   0.021549   0.710 0.478553    
## EthnoRace_AA   -0.019621   0.016959  -1.157 0.248947    
## Gender_0F_1M    0.008194   0.014602   0.561 0.575431    
## RAmy_BA10       0.514025   0.140124   3.668 0.000328 ***
## Internalizing   0.009809   0.014060   0.698 0.486331    
## pubc_mean       0.006775   0.012330   0.549 0.583443    
## VE:Deprivation -0.034307   0.015116  -2.270 0.024513 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07235 on 167 degrees of freedom
## Multiple R-squared:  0.1255, Adjusted R-squared:  0.07833 
## F-statistic: 2.662 on 9 and 167 DF,  p-value: 0.006544
cor.test(no_anxiety_dep_med_data$RAmy_BA47, no_anxiety_dep_med_data$ThreatCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_anxiety_dep_med_data$RAmy_BA47 and no_anxiety_dep_med_data$ThreatCompc
## t = -0.14213, df = 175, p-value = 0.8871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1579939  0.1369740
## sample estimates:
##         cor 
## -0.01074368
cor.test(no_anxiety_dep_med_data$RAmy_BA47,no_anxiety_dep_med_data$DepCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_anxiety_dep_med_data$RAmy_BA47 and no_anxiety_dep_med_data$DepCompc
## t = 0.5774, df = 175, p-value = 0.5644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1045676  0.1898849
## sample estimates:
##        cor 
## 0.04360558
summary(lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data =no_anxiety_dep_med_data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, 
##     data = no_anxiety_dep_med_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56456 -0.38299  0.03202  0.31050  1.93533 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.23256    0.30843   3.996 0.000102 ***
## RAmy_BA47     -2.74381    0.70270  -3.905 0.000145 ***
## Internalizing  0.18138    0.12687   1.430 0.154989    
## pubc_mean     -0.03832    0.09197  -0.417 0.677534    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6458 on 144 degrees of freedom
##   (29 observations deleted due to missingness)
## Multiple R-squared:  0.1045, Adjusted R-squared:  0.08587 
## F-statistic: 5.603 on 3 and 144 DF,  p-value: 0.00116
summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data=no_anxiety_dep_med_data))
## 
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + 
##     Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data = no_anxiety_dep_med_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.05728 -0.03001 -0.00735  0.01807  0.11962 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.0216889  0.0249664   0.869 0.386246    
## VE              0.0009942  0.0065925   0.151 0.880305    
## Deprivation     0.0012091  0.0075425   0.160 0.872834    
## EthnoRace_C    -0.0077174  0.0114494  -0.674 0.501216    
## EthnoRace_AA    0.0157903  0.0089628   1.762 0.079938 .  
## Gender_0F_1M    0.0014130  0.0077639   0.182 0.855809    
## RAmy_BA47       0.1450724  0.0395470   3.668 0.000328 ***
## Internalizing   0.0040167  0.0074736   0.537 0.591669    
## pubc_mean       0.0002370  0.0065565   0.036 0.971214    
## VE:Deprivation -0.0078888  0.0081305  -0.970 0.333310    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03844 on 167 degrees of freedom
## Multiple R-squared:  0.1386, Adjusted R-squared:  0.09219 
## F-statistic: 2.986 on 9 and 167 DF,  p-value: 0.002529
cor.test(no_anxiety_dep_med_data$RAmy_BA10, no_anxiety_dep_med_data$ThreatCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_anxiety_dep_med_data$RAmy_BA10 and no_anxiety_dep_med_data$ThreatCompc
## t = 0.34737, df = 175, p-value = 0.7287
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1217225  0.1730799
## sample estimates:
##       cor 
## 0.0262494
cor.test(no_anxiety_dep_med_data$RAmy_BA10,no_anxiety_dep_med_data$DepCompc)
## 
##  Pearson's product-moment correlation
## 
## data:  no_anxiety_dep_med_data$RAmy_BA10 and no_anxiety_dep_med_data$DepCompc
## t = 0.33453, df = 175, p-value = 0.7384
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1226782  0.1721386
## sample estimates:
##        cor 
## 0.02527989
summary(lm(Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, data =no_anxiety_dep_med_data))
## 
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, 
##     data = no_anxiety_dep_med_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88318 -0.33528  0.00011  0.33462  1.94332 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    1.16411    0.31591   3.685 0.000323 ***
## RAmy_BA10     -3.79210    1.34908  -2.811 0.005629 ** 
## Internalizing  0.16227    0.12971   1.251 0.212968    
## pubc_mean     -0.05028    0.09419  -0.534 0.594294    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6612 on 144 degrees of freedom
##   (29 observations deleted due to missingness)
## Multiple R-squared:  0.06122,    Adjusted R-squared:  0.04167 
## F-statistic:  3.13 on 3 and 144 DF,  p-value: 0.02762

It doesn’t seem like the medication for anxiety/depression/antipsychotics influence our effect, which is good.

Potentially look at mediation with the interaction, RAmy_BA47, and ramy activation.

I’d like to note here that I don’t think we really have the sample size to do this, but I guess it would be worth a shot.

#library(lavaan)
# 
# hayes7 <- ' # regressions
#             RAmy_BA47 ~ a1*ThreatCompc
#             Ramy_0035 ~ b1*RAmy_BA47
#             RAmy_BA47 ~ a2*DepCompc
#             RAmy_BA47 ~ a3*ThreatDepInt
#             Ramy_0035 ~ cdash*ThreatCompc
#             # mean of centered write (for use in simple slopes)
#               DepCompc ~ DepCompc.mean*1
#             # variance of centered write (for use in simple slopes)
#               DepCompc ~~ DepCompc.var*DepCompc
#             # indirect effects conditional on moderator (a1 + a3*a2.value)*b1
#               indirect.SDbelow := a1*b1 + a3*-sqrt(DepCompc.var)*b1
#               indirect.mean := a1*b1 + a3*DepCompc.mean*b1
#              indirect.SDabove := a1*b1 + a3*(sqrt(DepCompc.var)*1.5)*b1'
# fit model
#sem <- sem(model = hayes7, data = clean.data,
#se = "bootstrap", bootstrap = 1000)
# fit measures
#summary(sem,
#fit.measures = TRUE,
#standardize = TRUE, rsquare = TRUE)

So I think this is similar to where we ended up with M.P. and the MPlus expedition of a while ago. I just don’t think we have the power to detect this moderated mediation and it’s probably because of the make-up of the sample as well in terms of who is both high in social dep and violence exposure, but I’m not entirely sure that I’m doing this correctly.

#DEMONSTRATE HOW POWER A PRIORI
#GENERATING A POPULATION

library(stats)

n=100
reps=1000
sims=NULL
for(m in 1:reps)
{   
    x=rnorm(n,0,1)
    y=.3*x+rnorm(n,0,sqrt(1-.3^2))
    l=lm(y~x)
    cor=cor(x,y)
    slope=summary(l)$coefficients[2,1]
    se=summary(l)$coefficients[2,2]
    p=summary(l  )$coefficients[2,4]
    sig=ifelse(p<.05,1,0)
    est=cbind(slope,cor,se,p,sig)
    sims=rbind(sims,est)
}
    
fix(sims)

sampling.dist=data.frame(sims)

sampling.dist$slope
##    [1] 0.293174669 0.228791139 0.181371311 0.200154006 0.325103479
##    [6] 0.545297849 0.228792554 0.413846808 0.319241276 0.252866775
##   [11] 0.535881117 0.280909099 0.245258492 0.417219357 0.275737112
##   [16] 0.347642886 0.479719921 0.219622261 0.397274380 0.417306904
##   [21] 0.343832587 0.100312121 0.237986757 0.283780253 0.273040595
##   [26] 0.302602275 0.252448422 0.189240527 0.243232108 0.260281149
##   [31] 0.327207413 0.305637786 0.174280370 0.336252982 0.134163795
##   [36] 0.303996915 0.370145789 0.404442927 0.335859644 0.211981149
##   [41] 0.215842949 0.356378280 0.367775384 0.267863635 0.392496704
##   [46] 0.350062437 0.268586029 0.368477207 0.084040153 0.280462612
##   [51] 0.350616694 0.315015327 0.233330269 0.270816864 0.257032971
##   [56] 0.276956200 0.276259667 0.235475744 0.370631400 0.240952308
##   [61] 0.263015637 0.375729804 0.366816724 0.317664838 0.368692793
##   [66] 0.277762058 0.359369586 0.222142283 0.273928510 0.363286018
##   [71] 0.297681438 0.318607401 0.222938674 0.375585222 0.270670215
##   [76] 0.286065609 0.261333708 0.445572834 0.351231095 0.355092680
##   [81] 0.349995470 0.217468591 0.313195806 0.276015957 0.314507705
##   [86] 0.572600739 0.355287310 0.478993406 0.020498129 0.281464436
##   [91] 0.296723318 0.325770699 0.497606129 0.278552329 0.319792223
##   [96] 0.406495694 0.093296714 0.394529418 0.316021436 0.366255352
##  [101] 0.381317569 0.288251899 0.435488979 0.143500040 0.180242212
##  [106] 0.413234285 0.062373794 0.363685515 0.175417815 0.182252320
##  [111] 0.267105175 0.178265644 0.222870182 0.315918981 0.219145314
##  [116] 0.409346169 0.272089440 0.399298606 0.376678754 0.226738713
##  [121] 0.159484592 0.341037820 0.269951897 0.156235468 0.276456539
##  [126] 0.093764845 0.234406280 0.339662368 0.314120367 0.133979766
##  [131] 0.334976694 0.262605450 0.272669603 0.315829699 0.196589963
##  [136] 0.303621526 0.398268033 0.263577379 0.296556729 0.321439194
##  [141] 0.358038485 0.415362331 0.488602221 0.275076945 0.327667563
##  [146] 0.178357743 0.369290862 0.177165709 0.276743518 0.172924533
##  [151] 0.488909744 0.147825645 0.298455845 0.296155808 0.108435713
##  [156] 0.334636735 0.300111495 0.343942264 0.548592067 0.402458207
##  [161] 0.266459642 0.432587652 0.279216382 0.330143083 0.288988121
##  [166] 0.267146769 0.147206898 0.312746843 0.352745048 0.186379070
##  [171] 0.281493706 0.227071950 0.392893837 0.267598282 0.386595736
##  [176] 0.387268815 0.301399776 0.339670075 0.346391065 0.234473121
##  [181] 0.386800437 0.169533565 0.374876401 0.257886722 0.258420140
##  [186] 0.257241295 0.292423879 0.369247162 0.315624047 0.345277671
##  [191] 0.298695802 0.375100047 0.308026435 0.278371859 0.231118677
##  [196] 0.441146790 0.076094004 0.280867532 0.168814117 0.529558406
##  [201] 0.367568009 0.317408382 0.391746681 0.306285125 0.336559901
##  [206] 0.408244110 0.296713299 0.206567021 0.212293029 0.214380723
##  [211] 0.303031504 0.235098188 0.316863029 0.218300232 0.279698196
##  [216] 0.218928318 0.247319045 0.230890375 0.557191304 0.487358942
##  [221] 0.285906534 0.224607901 0.340265075 0.419895877 0.282477371
##  [226] 0.365933080 0.384435017 0.384012413 0.346825247 0.199819417
##  [231] 0.354732372 0.258668654 0.289614246 0.370109469 0.283902175
##  [236] 0.141542822 0.424728465 0.442134379 0.230227462 0.380491793
##  [241] 0.277795578 0.343658253 0.286529285 0.150784426 0.233952269
##  [246] 0.206666487 0.386356427 0.377821251 0.295073110 0.331892666
##  [251] 0.476919847 0.282250820 0.207772354 0.350412688 0.272319667
##  [256] 0.349962975 0.327796741 0.367721046 0.257229856 0.335053670
##  [261] 0.180913268 0.331359653 0.277365913 0.233516212 0.325899825
##  [266] 0.367142657 0.198471453 0.461623832 0.374150000 0.228016253
##  [271] 0.450756996 0.264937053 0.362729750 0.213845667 0.248645441
##  [276] 0.338565592 0.413826713 0.278445692 0.242586415 0.368444747
##  [281] 0.145288929 0.304695595 0.518593245 0.075123194 0.276472601
##  [286] 0.256885476 0.195507534 0.230998113 0.464973981 0.299697735
##  [291] 0.399887285 0.204490973 0.483565651 0.269177712 0.281508923
##  [296] 0.198231168 0.237539266 0.354062895 0.334502749 0.272029706
##  [301] 0.470994161 0.156711532 0.183281137 0.320839865 0.330873402
##  [306] 0.324329745 0.228674744 0.245329662 0.359231828 0.200812369
##  [311] 0.301806669 0.195752662 0.199328130 0.548880056 0.189728841
##  [316] 0.192756046 0.296640444 0.315865380 0.204602309 0.254437723
##  [321] 0.399230220 0.371455487 0.174224136 0.356176415 0.336050082
##  [326] 0.379350538 0.386603045 0.407874965 0.408781869 0.140019424
##  [331] 0.206712759 0.456518145 0.309574771 0.248849206 0.258753610
##  [336] 0.208117221 0.112259339 0.410864575 0.442122099 0.462173941
##  [341] 0.182186569 0.199178292 0.395941315 0.091285743 0.354499461
##  [346] 0.320784968 0.208992588 0.237112812 0.387474070 0.419392731
##  [351] 0.429115797 0.304722448 0.132033910 0.347093751 0.371574155
##  [356] 0.252861435 0.428739817 0.239836088 0.261577242 0.273912251
##  [361] 0.334062304 0.229597033 0.333222563 0.664307913 0.388957170
##  [366] 0.411653558 0.192641594 0.279719229 0.177016186 0.255924154
##  [371] 0.185762203 0.352232305 0.415513280 0.424946436 0.463556545
##  [376] 0.253907736 0.356428925 0.196691380 0.293388841 0.235359012
##  [381] 0.317266306 0.360376346 0.099687368 0.261135327 0.331746583
##  [386] 0.349948971 0.409509058 0.405689379 0.321073297 0.296614324
##  [391] 0.356221897 0.495592043 0.230775158 0.405024325 0.519037580
##  [396] 0.259754323 0.412321651 0.338207482 0.310904060 0.248711457
##  [401] 0.382473407 0.121154895 0.061854728 0.420811510 0.221894745
##  [406] 0.355189397 0.368940520 0.310677166 0.122006683 0.355264293
##  [411] 0.441959105 0.234060588 0.279136759 0.169322541 0.218905662
##  [416] 0.360775817 0.227335029 0.255940830 0.308046619 0.445729563
##  [421] 0.212887349 0.387208071 0.444771230 0.263234622 0.322229317
##  [426] 0.189066322 0.446574956 0.182804803 0.305831845 0.319366964
##  [431] 0.367806764 0.619107120 0.166507403 0.241217973 0.509297341
##  [436] 0.180085752 0.299164814 0.275488186 0.057796736 0.221446658
##  [441] 0.148821163 0.519485194 0.279388590 0.254087109 0.436539404
##  [446] 0.400875745 0.279380387 0.278866245 0.092707000 0.364918573
##  [451] 0.337274355 0.337784360 0.322448557 0.338224258 0.322802526
##  [456] 0.438582604 0.354830357 0.410731613 0.258734397 0.001356847
##  [461] 0.228825845 0.223535682 0.495500438 0.325464099 0.301721730
##  [466] 0.315040135 0.302627506 0.285142131 0.507283513 0.251693555
##  [471] 0.156953833 0.278258716 0.507431223 0.223615199 0.362991086
##  [476] 0.347602802 0.321666946 0.335551076 0.260756869 0.352106557
##  [481] 0.335848957 0.425406523 0.314113289 0.201264867 0.335183693
##  [486] 0.122483595 0.345817418 0.462033174 0.326638740 0.301641370
##  [491] 0.266154745 0.199295362 0.241326559 0.238798755 0.365784514
##  [496] 0.171291978 0.529433075 0.302962918 0.192852383 0.161732478
##  [501] 0.405467206 0.287073683 0.356828858 0.214235042 0.267106721
##  [506] 0.294277749 0.262328909 0.403425069 0.330201432 0.342752167
##  [511] 0.362151225 0.416553102 0.282662429 0.435349554 0.270012199
##  [516] 0.351503268 0.439813260 0.251939995 0.188747711 0.395880603
##  [521] 0.205928588 0.260737979 0.488874246 0.293668046 0.197756621
##  [526] 0.275776802 0.243604954 0.298005991 0.148350748 0.338020174
##  [531] 0.304368112 0.260608482 0.333352437 0.339760610 0.561426475
##  [536] 0.384950395 0.428559347 0.367917795 0.339791428 0.187251400
##  [541] 0.202097865 0.276684352 0.467635839 0.419208423 0.401455662
##  [546] 0.262197544 0.222703036 0.191101709 0.385442172 0.323579758
##  [551] 0.541432071 0.330510584 0.238614021 0.178515934 0.280571871
##  [556] 0.421858709 0.243489416 0.355753827 0.335440154 0.350961110
##  [561] 0.165577180 0.290260879 0.195946925 0.426553748 0.243420184
##  [566] 0.294792925 0.532381984 0.189134537 0.351236479 0.509691338
##  [571] 0.365619764 0.235202700 0.470892893 0.309085870 0.141993455
##  [576] 0.265061485 0.359312634 0.393261202 0.203887788 0.292990375
##  [581] 0.303052647 0.058819207 0.402056491 0.373838374 0.169497105
##  [586] 0.277091495 0.384580153 0.478294779 0.326845135 0.210342048
##  [591] 0.140367961 0.286023588 0.495650539 0.347888721 0.264384035
##  [596] 0.358433368 0.230443433 0.260991444 0.390231519 0.333024247
##  [601] 0.209883179 0.223392785 0.350395871 0.378567110 0.134210219
##  [606] 0.249383317 0.439363984 0.309180603 0.364809286 0.220355079
##  [611] 0.171062101 0.209889211 0.237473955 0.238976345 0.219705757
##  [616] 0.176464822 0.412874942 0.437797610 0.379638482 0.167654702
##  [621] 0.563985794 0.328533108 0.404247530 0.168449192 0.387233925
##  [626] 0.148378497 0.157355899 0.330613850 0.339495085 0.310571463
##  [631] 0.408094854 0.251779200 0.360770232 0.403097418 0.327501814
##  [636] 0.085967995 0.320504105 0.313233710 0.233302554 0.319539162
##  [641] 0.225278641 0.285958127 0.305820826 0.377097571 0.371286276
##  [646] 0.413969832 0.311253408 0.439781274 0.257986263 0.327142174
##  [651] 0.248583415 0.387181636 0.306020807 0.200570079 0.191751490
##  [656] 0.351476024 0.266585496 0.208187964 0.238572213 0.327147800
##  [661] 0.208478538 0.421072778 0.238599815 0.390119010 0.176823939
##  [666] 0.375142229 0.227591214 0.244004310 0.038698204 0.196145751
##  [671] 0.298804546 0.361858605 0.378098717 0.245834261 0.280992500
##  [676] 0.233917103 0.071081077 0.335319525 0.316522562 0.167389750
##  [681] 0.139692795 0.370340059 0.245629902 0.157160492 0.359139245
##  [686] 0.574751170 0.293648350 0.235983910 0.166876572 0.344343314
##  [691] 0.234644325 0.392319674 0.252549550 0.395700890 0.493845376
##  [696] 0.303643228 0.482070752 0.402821801 0.440754666 0.400645475
##  [701] 0.273958151 0.183346005 0.497081481 0.437078208 0.395806570
##  [706] 0.330637070 0.194499792 0.475213398 0.237199203 0.331291942
##  [711] 0.404510600 0.377770776 0.081424822 0.327186114 0.131358504
##  [716] 0.357085133 0.355933124 0.283908514 0.218739548 0.409592539
##  [721] 0.194632533 0.079806027 0.200861219 0.249933116 0.343914269
##  [726] 0.231717723 0.082895142 0.209820335 0.424053122 0.501978448
##  [731] 0.357917970 0.348766217 0.299269803 0.426109434 0.138391848
##  [736] 0.372748392 0.290378179 0.424905729 0.508413718 0.191985842
##  [741] 0.393631515 0.100325257 0.355182045 0.387853595 0.335809224
##  [746] 0.309705883 0.346110287 0.145738775 0.375694952 0.392933544
##  [751] 0.371470171 0.431747735 0.407442258 0.426756503 0.311522673
##  [756] 0.380661017 0.503537792 0.326861868 0.329455346 0.276332111
##  [761] 0.380424742 0.364116792 0.281388592 0.263977971 0.445064493
##  [766] 0.132038291 0.210135576 0.322482831 0.490692455 0.259802182
##  [771] 0.164725016 0.423858770 0.137530422 0.383927214 0.315147491
##  [776] 0.450960817 0.222154847 0.209776749 0.337724916 0.250355186
##  [781] 0.220319139 0.243262642 0.232957385 0.395106469 0.139929798
##  [786] 0.241220683 0.287918580 0.271305016 0.342277743 0.342735272
##  [791] 0.235474062 0.319849365 0.226286658 0.310987263 0.282245925
##  [796] 0.268291899 0.240483206 0.324347120 0.386060143 0.089822077
##  [801] 0.250563223 0.182414120 0.412966805 0.176337929 0.143222578
##  [806] 0.304570116 0.236856448 0.514765539 0.422478858 0.318539853
##  [811] 0.313918929 0.093759439 0.249667052 0.239868427 0.186228344
##  [816] 0.349047337 0.154985496 0.336519388 0.275646624 0.156932934
##  [821] 0.460680853 0.214162042 0.489593596 0.153481109 0.421199021
##  [826] 0.251443119 0.212674829 0.446543954 0.305162831 0.113356084
##  [831] 0.247728952 0.276523057 0.311459021 0.315822901 0.418493729
##  [836] 0.245509860 0.280530176 0.255736785 0.283143611 0.388672158
##  [841] 0.332873253 0.333516078 0.321069717 0.407379696 0.214481430
##  [846] 0.306105834 0.266653438 0.355029913 0.306577464 0.406099214
##  [851] 0.339740392 0.429973324 0.329561015 0.360245461 0.158107403
##  [856] 0.340006144 0.477567032 0.287192977 0.341122277 0.241345137
##  [861] 0.302799032 0.388882831 0.390973086 0.286398322 0.118554876
##  [866] 0.441798609 0.166373798 0.480805729 0.325817976 0.161636200
##  [871] 0.296988589 0.263789344 0.499208655 0.166684598 0.279686651
##  [876] 0.514063740 0.273381451 0.328818353 0.250966970 0.511111646
##  [881] 0.385366578 0.402194261 0.242956049 0.247356310 0.279660442
##  [886] 0.265530777 0.248638545 0.179862247 0.175537609 0.294560696
##  [891] 0.223823148 0.268236908 0.267491644 0.323596137 0.422881689
##  [896] 0.406017539 0.343438289 0.246171985 0.207847731 0.258340392
##  [901] 0.097566477 0.280844654 0.336349968 0.416344447 0.367247672
##  [906] 0.130818111 0.302734251 0.339216221 0.044812842 0.214946104
##  [911] 0.376476903 0.287665101 0.394846855 0.455058335 0.289176351
##  [916] 0.135707286 0.192037101 0.533690228 0.198640539 0.393546021
##  [921] 0.285208264 0.247623892 0.364387527 0.553981485 0.232394362
##  [926] 0.291070225 0.464044895 0.332977699 0.277188258 0.262265652
##  [931] 0.158601756 0.261526949 0.271292349 0.284864476 0.358869723
##  [936] 0.325897139 0.414483638 0.327050821 0.232603701 0.315733285
##  [941] 0.221910920 0.273957887 0.239429676 0.316403214 0.309880789
##  [946] 0.388573231 0.344127429 0.404822853 0.266407203 0.319840749
##  [951] 0.086964886 0.342335630 0.381431127 0.341954435 0.291516536
##  [956] 0.230305903 0.246643974 0.446020569 0.446133921 0.361227475
##  [961] 0.249183758 0.451269541 0.260010688 0.241189410 0.412330899
##  [966] 0.288291772 0.290773489 0.378477882 0.177457192 0.236871546
##  [971] 0.329085890 0.523503885 0.215155030 0.381584463 0.271442332
##  [976] 0.340619369 0.395797405 0.227235510 0.137459051 0.366477565
##  [981] 0.361499189 0.117656835 0.400840082 0.328301650 0.272497364
##  [986] 0.169789690 0.396401272 0.260710975 0.340611624 0.403418049
##  [991] 0.186061967 0.254762387 0.340783951 0.233991774 0.314620882
##  [996] 0.086634587 0.118069324 0.370651342 0.339438785 0.340991446
hist(sampling.dist$slope)

table(sampling.dist$sig)
## 
##   0   1 
## 133 867
mean(sampling.dist$sig)
## [1] 0.867
    #This represents power for the model